Customer Experience

It is hard to talk about survey methodology and practices without mentioning the Likert scale. While some may think the Likert scale is only used in academic research, it is a cornerstone of survey strategies across various industries such as travel & hospitality, automotive, and financial services.

What is the Likert Scale?

The Likert Scale, named after psychologist Rensis Likert, is a widely used tool in social science research and survey methodology for measuring attitudes, opinions, and perceptions of respondents. The Likert Scale usually ranges from five to seven points, with respondents selecting a response that best reflects their agreement or disagreement with each statement. The typical format includes options such as “Strongly Disagree,” “Disagree,” “Neutral,” “Agree,” and “Strongly Agree.” In some cases, scales may also include “Don’t Know” or “Not Applicable” options.

Researchers analyze the responses to calculate measures of central tendency (like mean or median) and dispersion (like standard deviation) to understand the distribution of opinions or attitudes within the sample population. This scale provides a structured way to quantify subjective opinions, making it easier to analyze and compare data across respondents and groups.

What are the Different Types of Likert Scales?

There are several variations of Likert scales, differing primarily in the number of response options provided to respondents. The two most common types are the 5-point Likert scale and the 7-point Likert scale.

5-Point Likert Scale:

In this scale, respondents are typically presented with a statement and five response options ranging from “Strongly Disagree” to “Strongly Agree.” The options might look like this:

• Strongly Disagree
• Disagree
• Neither Agree nor Disagree (Neutral)
• Agree
• Strongly Agree

7-Point Likert Scale:

The 7-point Likert scale expands on the 5-point scale by providing additional response options, usually to offer more nuanced distinctions between levels of agreement and disagreement. The options might look like this:

• Strongly Disagree
• Disagree
• Somewhat Disagree
• Neither Agree nor Disagree (Neutral)
• Somewhat Agree
• Agree
• Strongly Agree

Both scales serve the same purpose of measuring attitudes or opinions, but the 7-point Likert scale allows for a finer granularity of responses, which can sometimes provide more detailed insights into respondents’ attitudes or perceptions. The choice between the two scales depends on the specific needs of the research or survey design and the level of detail desired in the responses.

What is the Best Type of Likert Scale to Use?

The choice of which Likert scale to use depends on several factors, including the research objectives, the nature of the survey questions, and the preferences of the researcher or organization conducting the survey. There isn’t a universally “best” type of Likert scale; rather, it’s about selecting the most appropriate scale for the specific context. Here are some considerations to keep in mind when choosing a Likert scale:

Research Objectives

Consider the goals of your research and the type of data you need to collect. If you require more nuanced responses to accurately capture the variability in respondents’ attitudes or opinions, a 7-point Likert scale might be more suitable. However, if simplicity and ease of interpretation are priorities, a 5-point Likert scale could suffice.

Question Complexity

The complexity of the survey questions can influence the choice of the Likert scale. If the questions are straightforward and do not require fine-grained distinctions in responses, a simpler scale like the 5-point Likert scale may be sufficient. On the other hand, if the questions are more complex or cover a wide range of opinions, a 7-point Likert scale might provide more flexibility.

Response Bias

Consider the potential for response bias in your survey. Providing more response options (e.g., with a 7-point Likert scale) can sometimes reduce the likelihood of respondents selecting neutral options as a default. However, too many response options could overwhelm respondents and lead to careless responses.

Comparison with Existing Data

If you have existing data or are conducting research in a field where a particular Likert scale is commonly used, it may be advantageous to maintain consistency for easier comparison and analysis across studies.

Ultimately, the choice of the Likert scale should be made thoughtfully, taking into account the specific requirements of the research, the characteristics of the respondents, and the overall survey design. It’s often beneficial to pilot test different versions of the Likert scale to gauge respondent understanding and ensure the scale effectively captures the intended attitudes or opinions.

Examples of Likert Scale Questions

Writing effective Likert scale questions involves careful consideration of the topic, clarity of language, and ensuring that response options adequately capture the range of attitudes or opinions you want to measure. These factors are of the utmost importance to limit any type of voluntary response bias in sampling. Remember, whoever answers the question will be answering by selecting a range of emotions such as “satisfied/agree” or “not satisfied/disagree.” So, more often than not, these questions will be statements that reflect aspects of the topic you are trying to assess. Here are some examples of Likert scale questions:

• I am likely to recommend this product to others.
• The quality of the product meets my expectations.
• I am happy with the level of support provided by customer service.
• How pleased are you with your job?
• I thought this system was easy to use.

These examples represent Likert questions that can be direct questions or statements about a range of products and services. 

Examples of Bad Likert Scale Questions

Poorly constructed Likert questions often consist of double-barreled statements that contain ambiguous language that causes them to be biased or misleading. Consider the following examples:

• “Do you agree that the product is excellent and worth recommending?”

This question is double-barreled, combining two distinct concepts (“excellent” and “worth recommending”) into a single statement. This question would not yield a meaningful response as the question is comparing two items into one question. 

• “How much do you like the product: very much, much, somewhat, little, very little?”

This question lacks a clear direction or anchor for respondents to understand the meaning of each response option. It also uses imprecise language (e.g., “somewhat”) that may be interpreted differently by respondents. This question would also not yield a meaningful response. 

How to Analyze Likert Scale Data

After your surveys have been completed, it is time to analyze the data. When it comes to analyzing Likert scale data, there are a number of ways to segment the data. Which method you choose will ultimately end on the initial research questions. Some examples of this data analysis are descriptive, frequency, and regression analysis. 

• Descriptive analysis: Calculate the mean, median, mode, and standard deviation for each response on the Likert scale for a quick summarization of the data. 
• Frequency analysis: Total the number of items each response was selected and use the quantitative data to create tables or charts to show the distribution of each answer. 
• Regression analysis: Depending on the objective of the survey, you may be able to analyze the relationship between the various Likert responses and an independent variable. 

Advantages of Using the Likert Scale

The Likert scale offers several advantages for organizations that are looking to implement a simple, effective survey methodology. Likert scales are straightforward and easy to understand for both respondents and researchers. Along with ease of use, here are some other benefits of utilizing the Likert scale: 

• Flexibility: Likert scales can be adapted to measure a wide range of constructs, including attitudes, opinions, behaviors, satisfaction levels, and more. Researchers can customize Likert scale questions to fit their specific research objectives and contexts.
• Comparability: Likert scale data enables researchers to compare responses across different groups, variables, or time points. This comparability facilitates meaningful analysis of trends, differences, or relationships within the data.
• Standardization: Likert scales provide a standardized format for measuring attitudes or opinions, enhancing the consistency and replicability of research findings. This standardization allows for easier comparison of results across studies and populations.

Limitations of the Likert Scale

The Likert scale offers many advantages, but those are not without a small set of limitations. One of the biggest limitations of the Likert scale is the finite number of responses that respondents are limited to. These may not fully capture the complexity of respondents’ attitudes or opinions. This can lead to oversimplification or loss of nuance in the data.

Along with this, respondents may exhibit response bias, such as acquiescence bias (tendency to agree with statements) or social desirability bias (tendency to provide socially acceptable responses), particularly if the scale lacks anonymity or if respondents feel pressured to conform to perceived norms.

Despite these limitations, the Likert scale remains a widely used and valuable tool for measuring attitudes, opinions, and perceptions in various research settings. Researchers should carefully consider these limitations and take steps to mitigate potential biases and challenges when designing and interpreting Likert scale surveys.

When to Use the Likert Scale

Likert scales are well-suited for assessing individuals’ attitudes or opinions toward specific topics, issues, products, services, or experiences. This can come in the form of a Net Promoter Score (NPS) survey or a Customer Satisfaction Survey (CSAT). For example, they can be used to gauge satisfaction with customer service or perceptions of organizational culture. 

Furthermore, Likert scales are effective in quantifying subjective perceptions or experiences. Researchers can use Likert scales to measure perceptions of quality, trust, reliability, fairness, or effectiveness in various domains. This can be used to ask customers about their personal experiences with an organization and make those answers measurable. 

How the Likert Scale Effects Your CX Efforts

The Likert scale is a great tool to be utilized in your customer experience efforts. They are a great way to provide a structured method for measuring customer satisfaction across various touchpoints in the customer journey. By asking customers to rate their satisfaction levels with specific aspects of their experience (e.g., product quality, service responsiveness, website usability), organizations can identify areas of strength and areas for improvement.

Similarly, Likert scale data provides valuable insights that can inform strategic decision-making and resource allocation. By identifying areas with low satisfaction scores or high variability in responses, organizations can prioritize investments in CX improvement initiatives that are most likely to have a positive impact on customer loyalty and retention. 

Involving customers in the feedback process through Likert scale surveys can enhance engagement and satisfaction. By demonstrating a commitment to listening to customer feedback and taking action based on their responses, organizations can build trust, loyalty, and advocacy among their customer base.

Utilize the Likert Scale with InMoment

InMoment’s XI Platform allows you to utilize the Likert Scale to gather actionable feedback, measure satisfaction, and drive meaningful improvements. Schedule a demo today to see how we can help your business. 

When you think of probability sampling, you may think about statistical analysis and research studies. However, probability sampling can be a great tool for CX practitioners because it allows them to systematically collect feedback from representative samples of customers, which enables them to gain deeper insights into customer needs, preferences, and satisfaction levels. 

By using probability sampling methods, CX practitioners can make data-driven decisions, identify areas for improvement, and tailor products and services to better meet customer expectations, ultimately enhancing the overall customer experience.

What is Probability Sampling?

Probability sampling is a method used in statistics to select a subset of individuals or items from a larger population in such a way that every individual or item has a known, non-zero probability of being chosen. In other words, each member of the population has a chance of being selected, and this chance can be quantified.

What is the Goal of Probability Sampling?

The goal of probability sampling is to obtain a sample that accurately represents the larger population from which it is drawn. By ensuring that every member of the population has a chance of being selected, probability sampling allows researchers to make statistical inferences about the population based on the characteristics of the sample. This helps to minimize bias and increase the reliability of the conclusions drawn from the sample.

What are the Different Types of Probability Sampling?

There are various probability sampling methods, the four most common types are simple random sampling, stratified sampling, systematic sampling, and cluster sampling. Each type of probability sampling has its own strengths and weaknesses, and the choice of method depends on factors such as the nature of the population, the resources available, and the goals of the research.

Simple Random Sampling 

In simple random sampling, every individual in the population has an equal chance of being selected, and each selection is made independently of the others. This can be achieved by methods such as random number generators or drawing names from a hat. Simple random sampling is straightforward and ensures that each member of the population has an equal opportunity to be included in the sample.

Stratified Sampling

When it comes to stratified sampling, the population is divided into subgroups or strata based on certain characteristics that are relevant to the research (e.g., age, gender, income level). Then, a simple random sample is taken from each stratum. This ensures that each subgroup is represented proportionally in the sample, which can increase the precision of estimates for each subgroup and the overall population.

Systematic Sampling

In systematic sampling, individuals are selected from the population at regular intervals after a random start. For example, if you have a population of 1000 and want a sample size of 100, you might select every 10th individual after randomly selecting a starting point between 1 and 10. Systematic sampling can be more convenient than simple random sampling and still provide a representative sample if the population is ordered in some way.

Cluster Sampling

In cluster sampling, the population is divided into clusters (e.g., geographical areas, schools, households) and then a random sample of clusters is selected. All individuals within the chosen clusters are included in the sample. Cluster sampling can be more practical and cost-effective than other methods, especially when the population is large and dispersed. However, it may introduce more variability because individuals within the same cluster may be more similar to each other than to individuals in other clusters.

What Probability Sampling Method is Best?

The “best” probability sampling method depends on various factors including the nature of the population, the research objectives, resource constraints, and practical considerations. There isn’t a one-size-fits-all answer, as each method has its own advantages and limitations. However, researchers typically choose the method that best balances accuracy, feasibility, and cost-effectiveness for their specific study. Here is a quick overview of when to use each method: 

• Simple Random Sampling: This method is ideal when each member of the population is equally important to the study and there are no relevant subgroups or strata to consider. It’s straightforward and easy to implement but may not be practical for large or geographically dispersed populations.

• Stratified Sampling: If the population can be divided into meaningful subgroups or strata based on relevant characteristics, stratified sampling can improve the precision of estimates for each subgroup and the overall population. It’s particularly useful when there is variability within the population and when researchers want to ensure representation from each subgroup.

• Systematic Sampling: Systematic sampling is convenient and practical when the population is ordered in some way, such as in a list or a sequence. It’s easy to implement and may provide a representative sample if the order doesn’t introduce bias. However, it can be sensitive to periodic patterns in the data.

• Cluster Sampling: Cluster sampling is useful when the population is large and dispersed, making it impractical or costly to sample individuals directly. It can reduce costs and logistical challenges by sampling groups or clusters of individuals. However, it may introduce more variability because individuals within the same cluster may be more similar to each other than to individuals in other clusters.

Ultimately, the choice of probability sampling method should be guided by careful consideration of the specific research context and goals, as well as practical constraints such as budget, time, and available resources.

How to Conduct Probability Sampling

When conducting probability sampling, it is important that you go about it the right way to ensure that your findings are a complete and accurate representation of your sample. Here is a quick overview of the steps to conduct probability sampling: 

• Define the Population: Clearly define the population of interest for your study. This is the entire group that you want to make inferences about.

• Identify Sampling Frame: Create a list or other representation of the population from which you will draw your sample. This is known as the sampling frame. It should include all individuals or items in the population.

• Choose a Sampling Method: Select a probability sampling method that is appropriate for your study and population. Consider factors such as the nature of the population, available resources, and research objectives.

• Determine Sample Size: Decide on the size of your sample, which should be large enough to provide reliable estimates but small enough to be manageable within your constraints.

• Select Sampling Units: Use the chosen sampling method to select sampling units from the sampling frame. Ensure that each unit has a known, non-zero probability of being selected.

• Implement Sampling Procedure: Select the sample units according to the chosen sampling method. This might involve random selection, stratification, systematic sampling, or clustering, depending on the method chosen.

• Collect Data: Once the sample has been selected, collect data from each sampled unit. Ensure that data collection procedures are standardized and consistent across all units.

• Analyze Data: Analyze the data collected from the sample using appropriate statistical methods. Make inferences about the population based on the characteristics of the sample.

• Draw Conclusions: Draw conclusions about the population based on the results of your analysis. Be sure to consider the limitations of your sample and any potential sources of bias.

• Report Findings: Finally, report your findings, including details about the sampling method used, sample size, and any limitations or assumptions made. Provide enough information to allow others to assess the validity and generalizability of your results.

Probability vs Non-probability Sampling

The primary difference between probability and non-probability sampling lies in how the sample is selected and the extent to which the selection process allows for the generalization of results to the larger population.

In probability sampling, every individual or item in the population has a known, non-zero chance of being selected for the sample. Each member of the population has an equal opportunity of being chosen, and the selection is based on random processes. Results from probability sampling can be generalized to the larger population with a known degree of confidence, assuming proper sampling techniques and randomization.

In non-probability sampling, the selection of individuals or items for the sample does not involve random processes, and not every member of the population has a known chance of being selected. Non-probability sampling methods include convenience sampling, purposive sampling, snowball sampling, and quota sampling. Results from non-probability sampling cannot be statistically generalized to the larger population with the same level of confidence as probability sampling. Instead, they are typically considered exploratory or descriptive in nature and may be subject to various biases.

Advantages and Disadvantages of Probability Sampling

Probability sampling is a useful technique and should be utilized frequently. However, when conducting probability sampling, you should be aware of the advantages and disadvantages of doing so. 

Advantages

• Representativeness: Probability sampling methods ensure that each member of the population has a known chance of being selected for the sample. This helps to create a sample that is more likely to be representative of the larger population.

• Generalizability: Because probability sampling provides a representative sample, the results obtained from the sample are more likely to be generalizable to the entire population. This allows researchers to make valid statistical inferences about the population based on the characteristics of the sample.

• Statistical Inference: Probability sampling allows for the calculation of statistical measures such as sampling error, confidence intervals, and p-values. This enables researchers to quantify the uncertainty associated with their estimates and draw more reliable conclusions.

• Randomization: Probability sampling methods typically involve random selection processes, which help to minimize selection bias and ensure that the sample is not systematically skewed in one direction.

• Precision: Probability sampling methods such as stratified sampling can improve the precision of estimates by ensuring adequate representation of different subgroups within the population.

Disadvantages

• Resource Intensive: Probability sampling methods can be more resource-intensive and time-consuming compared to non-probability sampling methods, especially for large or dispersed populations.

• Complexity: Some probability sampling methods, such as stratified or cluster sampling, can be more complex to implement and require careful planning and coordination.

• Sampling Frame Required: Probability sampling methods require a comprehensive sampling frame that includes all members of the population. If the sampling frame is incomplete or inaccurate, it can introduce bias into the sample.

• Practical Constraints: In some cases, it may be impractical or impossible to obtain a probability sample due to resource constraints, logistical challenges, or the nature of the population.

• Sampling Error: While probability sampling aims to minimize sampling error, it cannot eliminate it entirely. Variability within the population and sampling variability can still affect the accuracy of estimates obtained from the sample.

Probability sampling offers the advantage of providing representative and generalizable results, but it may be more resource-intensive and complex to implement compared to non-probability sampling methods. Careful consideration of the advantages and disadvantages of probability sampling is necessary when designing a research study.

How Probability Sampling Can Improve the Customer Experience

Probability sampling can be leveraged to improve the customer experience in several ways. By using probability sampling methods such as stratified sampling, businesses can ensure that they capture a diverse range of customer opinions and preferences. This allows them to gain a deeper understanding of their customers’ needs and expectations.

Probability sampling also allows businesses to measure customer satisfaction using statistically valid methods. By regularly surveying a representative sample of customers, businesses can track changes in satisfaction levels over time and identify trends or patterns that may impact the customer experience.

See how you can conduct probability sampling in InMoment’s XI Platform by scheduling a demo today!

Market research and market segmentation is a crucial part of launching any campaign or product. One part of this process that is often overlooked is how market segments are developed. It is important to use proper sampling techniques to gain the most accurate market segmentation results. One of these techniques is stratified sampling. 

Stratified sampling provides businesses with a nuanced understanding of customer preferences and behaviors within each segment, allowing for the development of personalized marketing strategies. By tailoring marketing messages, promotions, and campaigns to specific customer segments, businesses can increase relevance and engagement, ultimately enhancing the overall customer experience.

What is Stratified Sampling?

Stratified sampling involves dividing a population into subgroups or strata based on certain characteristics that are relevant to the research objectives. These characteristics could include demographics, geographic location, purchasing behavior, or any other pertinent factors. Once the population is segmented, researchers can then randomly sample from each subgroup to ensure representation across all strata.

What is the Purpose of Stratified Sampling?

The primary purpose of stratified sampling is to reduce sampling variability and increase the precision of estimates by ensuring that each subgroup of the population is adequately represented in the sample. By targeting specific strata, researchers can capture the diversity within the population and draw more accurate conclusions from the data collected.

How to Conduct Stratified Sampling?

To conduct stratified sampling effectively, researchers must first identify the relevant stratification variables based on the research objectives. Once the strata are defined, researchers determine the sample size for each stratum based on its proportion within the population. Then, random samples are drawn from each stratum to form the overall sample.

What is an Example of Stratified Sampling?

Consider a cosmetics company that aims to develop new skincare products tailored to the specific needs and preferences of different age groups within its customer base. To achieve this, they decide to conduct a market research study using stratified sampling.

This company would begin by identifying age as the stratification variable. They divide their customer base into distinct age groups, such as:

• 18-25 years old
• 26-35 years old
• 36-45 years old
• 46 years old and above

Next, they determine the proportion of customers in each age group based on their customer database or previous sales data. Let’s say they find that their customer distribution across age groups is as follows:

• 18-25 years old: 30%
• 26-35 years old: 35%
• 36-45 years old: 25%
• 46 years old and above: 10%

Based on these proportions and the desired sample size, this company calculates the number of respondents needed from each age group to ensure adequate representation.

Once the sample sizes for each age group are determined, this company selects a random sample of customers from each stratum. For example, if they need 100 respondents in total:

• From the 18-25 age group: 30 respondents
• From the 26-35 age group: 35 respondents
• From the 36-45 age group: 25 respondents
• From the 46+ age group: 10 respondents

They can then reach out to these selected customers through surveys, focus groups, or interviews to gather their opinions, preferences, and skincare needs.

After collecting the responses, they analyze the Voice of the Customer data within each age group separately. They can identify common trends, preferences, and pain points within each demographic segment.

Armed with insights from the stratified sample, they can develop targeted marketing campaigns and skincare products tailored to the specific needs and preferences of each age group. For instance, they might find that younger customers prefer lightweight, hydrating formulas, while older customers prioritize anti-aging benefits and skincare products with rich textures.

By using stratified sampling, this company ensures that its market research is comprehensive and representative of its diverse customer base. This approach allows them to make informed decisions and create products that resonate with each segment of their audience, ultimately enhancing the overall customer experience.

Advantages of Stratified Sampling

Stratified sampling offers several advantages over other sampling methods, including increased precision, reduced bias, enhanced generalizability, detection of subgroup differences, and efficient resource allocation. By leveraging these benefits, researchers can obtain more accurate and actionable insights from their data, ultimately leading to better-informed decision-making and improved outcomes. Here are some advantages of stratified sampling:

Increased Precision

One of the primary advantages of stratified sampling is its ability to increase the precision of estimates by ensuring representation from all subgroups or strata within the population. By dividing the population into homogeneous groups based on relevant characteristics, such as demographics or behavior, researchers can capture the variability within each stratum more effectively. This precision leads to more accurate and reliable results compared to simple random sampling, especially when there are significant differences between subgroups.

Reduced Sampling Bias

Stratified sampling helps mitigate various biases, such as voluntary response bias, by ensuring that each subgroup of the population is adequately represented in the sample. This reduces the risk of over-representing or under-representing certain segments of the population, which can skew the results and lead to erroneous conclusions. By sampling proportionally from each stratum, researchers can obtain a more balanced and representative sample, thereby minimizing bias and increasing the validity of the findings.

Enhanced Generalizability

Because stratified sampling ensures representation from all subgroups within the population, the results are often more generalizable or applicable to the entire population. By capturing the diversity of characteristics and perspectives across different strata, researchers can draw conclusions that are more robust and applicable to a broader range of individuals or entities. This enhanced generalizability makes the findings from stratified sampling more valuable for informing decision-making and guiding actions within the population of interest.

Detection of Subgroup Differences

Another advantage of stratified sampling is its ability to detect differences or patterns within specific subgroups of the population. By analyzing the data separately for each stratum, researchers can identify unique trends, preferences, or behaviors that may exist within certain demographic or behavioral segments. This granularity allows for a deeper understanding of the population dynamics and can inform targeted interventions or strategies tailored to the needs of different subgroups.

Efficient Resource Allocation

Stratified sampling can also lead to more efficient resource allocation by focusing data collection efforts on the most relevant subgroups or strata within the population. Instead of using resources indiscriminately across the entire population, researchers can prioritize areas of interest or importance based on the stratification variables. This targeted approach not only saves time and resources but also maximizes the utility of the data collected, resulting in a more cost-effective research process.

Types of Sampling

There are various different sampling methods and each sampling method has its own advantages and limitations, and the choice of method depends on various factors such as the research objectives, the characteristics of the population, resource constraints, and the desired level of precision and generalizability. Researchers must carefully consider these factors when selecting the most appropriate sampling method for their study. Here are some common examples of different types of sampling. 

Random Sampling

Random sampling, or simple random sampling, involves selecting individuals from a population entirely by chance, where each member of the population has an equal probability of being chosen. This method is widely used because it is relatively easy to implement and helps to reduce bias in the selection process. Random sampling can be done with or without replacement, meaning that individuals may or may not be returned to the population after selection.

What is Stratified vs Random Sampling?

Stratified sampling and random sampling are two different approaches to selecting a sample from a population for research purposes. Stratified sampling involves dividing the population into subgroups or strata based on specific characteristics that are relevant to the research objectives. Samples are then independently drawn from each stratum, ensuring representation from all segments of the population. On the other hand, random sampling involves selecting individuals from a population entirely by chance, where each member of the population has an equal probability of being chosen. 

Systematic Sampling

Systematic sampling involves selecting every nth individual from a population after starting with a random sample. For example, if a researcher wants to sample every 10th person from a list of customers, they would randomly select a starting point and then select every 10th person thereafter. Systematic sampling is efficient and straightforward, but it may introduce bias if there is a pattern or periodicity in the population.

Stratified Sampling

As discussed earlier, stratified sampling involves dividing the population into subgroups or strata based on relevant characteristics and then sampling from each stratum proportionally. This method ensures representation from all segments of the population and increases the precision of estimates by capturing variability within each stratum.

Use Stratified Sampling with InMoment

Any good CX program is built on the foundation of understanding your customer. By using stratified sampling methods with InMoment’s Market Experience Software, you can work to conduct more effective market research to make sure you are on the right track for improving your customer experience. Schedule a demo to see what InMoment can do for you today!

In the midst of today’s bustling and intricate business landscape, deciphering the ever-evolving wants and needs of customers can feel like navigating through a maze. However, creating an integrated CX program can make this easier than you might think. At InMoment, we are dedicated to delivering tangible business value and bolstering your bottom line through a comprehensive integrated CX approach.

What is Integrated Customer Experience (CX)?

Integrated CX is all about harnessing the power of data, technology, and expert service to help companies unlock valuable insights so they can take action to drive measurable outcomes for their customers. It’s creating an integrated customer experience by seamlessly bringing together a wealth of information, utilizing cutting-edge technology, and providing top-notch service to reveal the hidden gems within your customer experience. By merging these elements, Integrated CX empowers businesses to make informed decisions, improve customer satisfaction, and drive success in an increasingly data-driven world. In short, integrated customer experience is an anti-siloed CX strategy. 

Integrated CX vs. CX Integrations

While integrated CX and CX integrations may sound similar, they have different meanings and applications for businesses looking to improve their customer engagement. 

Integrated customer experience revolves around breaking down data silos and consolidating customer data from diverse sources into a unified and accessible repository. The goal is to create a comprehensive view of the customer, drawing insights from various touch points such as interactions, purchases, and feedback. By amalgamating data from sources like sales, marketing, and customer support, integrated CX provides a holistic perspective, enabling organizations to understand customer behavior and preferences more thoroughly.

On the other hand, customer experience integrations focus on the collaborative efforts of different software applications to amplify the capabilities of customer experience management. Instead of concentrating on data consolidation, CX integrations emphasize the interoperability of software solutions. This involves integrating various tools and platforms to streamline processes, automate workflows, and enhance overall efficiency in delivering exceptional customer experiences.

In essence, integrated CX is about centralizing customer data for a unified view, while CX integrations focus on the integration of diverse software tools to enhance the capabilities of the customer experience. 

Benefits of Integrated Customer Experience

Most businesses think that integrated CX is a practice that realizes very little monetary value. However, that couldn’t be further from the truth. Integrated customer experience is a catalyst that improves organizations’ main metrics and bottom line. 

As a matter of fact, a study of over 10,000 CX practitioners showed that those who used holistic data sets to make customer decisions also saw an increase in metrics such as: 

• 91% Higher NPS Score
• 89% Higher Retention
• 93% More Profitable 

Developing an integrated customer experience strategy will lead to higher levels of efficiency and engagement in your employees that will reflect in customer interactions. This alignment can lead to 2.4x more revenue growth in your business. 

With this data, it is clear that integrated CX is a strategic investment that pays off in improved customer relationships, operational efficiency, and overall business success. 

Explore the potential of your customer experience ROI with InMoment’s ROI calculator. This tool allows you to estimate the return on investment you could achieve by leveraging our solutions, helping you make informed decisions and optimize your CX initiatives for maximum impact and profitability. Find out how much value you can unlock for your business below.

Calculate your business’s ROI using InMoment’s VoC tools.

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What Are the Key Elements that Make Up Integrated Customer Experience?

Integrated customer experience is composed of three main elements. These elements help businesses make sense of all types of data throughout the customer journey to help you make informed decisions. Here are the three main elements of integrated CX:

1. Connect and collect data from every touchpoint in the customer journey
2. Interpret holistic structured and unstructured data to know where to focus for the highest business impact
3. Align cross-functional teams and balance automated and human actions to resolve individual cases and systemic issues

1. Capture and Connect Data From Every Source

InMoment’s first pillar of integrated customer experience is achieved by consolidating Voice of the Customer (VoC) data and non-customer data across the full customer journey by leveraging all forms of customer feedback channels. 

In order to be successful in today’s business environment, you need to leverage all your data, not just survey data. While surveys are an important first step, to get a comprehensive analysis of your VoC, you need data from all channels including:

• Call transcripts
• Emails
• Employee feedback
• Online chats
• Reviews
• Social media
• Surveys
• Support tickets

For example, imagine a customer named Sarah who frequently shops at an online clothing store. After her most recent purchase, she reported that she was “extremely dissatisfied” with her experience in a transactional customer experience survey. If you were to just stop there, you wouldn’t know why she was dissatisfied. 

However, if you were to look at her online chat data, you can see she was inquiring about getting the number to contact a customer service representative because her product was lost in shipment to her home address. 

Furthermore, if you were to look at her purchase history, her purchase was actually a repurchase of a product she had rated 5 stars in the past and described as “the most comfortable shirt that I own.” 

Based on these insights from multiple feedback channels, this retail brand can make sure Sarah’s order reaches her home address, give her product recommendations based on her past reviews, and contact the distribution department to make sure mistakes like this don’t happen in the future. 

These real-life scenarios happen more often than not and are often missed or provide misleading data.  For a real-world example, check out how Foot Locker partnered with InMoment to create an integrated customer experience program and boost their customer experience.

2. Identify the Richest Insights

This unified view of data serves as a powerful compass, guiding your organization toward faster, more impactful action. With all your customer data neatly organized and accessible in one place, the once overwhelming task of deciphering customer sentiments, behavior patterns, and preferences becomes a streamlined process. This puts you on the right track to creating a complete integrated customer experience program. 

From here, you need to utilize best-in-class AI technology and expert guidance from customer experience experts to help you sort through large amounts of customer data and identify key trends such as:

• Areas for process improvement
• Potential pain points in the customer journey 

This technology identifies critical trends that may have previously flown under the radar. Data-driven clarity empowers your organization to make informed decisions with confidence.

Using these methods, you can improve the decisions made from both structured and unstructured customer feedback. You can even associate this feedback with important customer experience KPIs such as churn rate, average purchase amount, and time to resolution. 

3. Unlock the Smartest Actions

Once you have sifted through the data, you can work to automate elements of your customer experience program. By automating elements of your customer experience program, you can significantly reduce the time it takes to execute various tasks. Through automation, processes that once required manual intervention, such as sending personalized follow-up emails, analyzing customer feedback, or triggering targeted marketing campaigns based on customer behavior, can now be executed swiftly and efficiently. This not only accelerates the speed at which you can respond to customer needs but also frees up valuable time and resources for your team to focus on strategic initiatives and high-impact activities.

By automating those tasks, you also have more time to empower multiple stakeholders in the overall strategic decisions behind your customer experience efforts. Whether it’s tracking customer acquisition costs, monitoring sales conversion rates, or analyzing customer lifetime value, access to relevant and actionable data empowers stakeholders to collaborate effectively and optimize strategies to maximize overall company ROI.

How Does Integrated Customer Experience Work?

To sum it all up, integrated CX focuses on three key drivers that help businesses improve their bottom line. The three most important are:

• Integrated signals: Bringing together the voice of customer data and non-customer data across the full customer lifecycle from surveys, chats, reviews, calls, etc
• Integrated Insights: Delivering both leading technology and strategic expertise to deliver business insights that lead to ROI
• Integrated Action: Eliminating the silos that exist in many companies, facilitating a coordinated, data-driven approach to prioritizing action

These three elements help deliver an integrated customer experience that drives sustainable growth and customer loyalty. With each key component, there is more than meets the eye. Let’s dive deeper into each one to explore how InMoment achieves customer experience success through integrated CX.

How to Set Up Your Business for Integrated CX 

Setting up your business for integrated customer experience requires a strategic approach that encompasses technology, processes, and a customer-centric mindset. Here are some things you need to do to set your business up for success:

1. Define Your Customer Touch Points

Start by mapping out all the touchpoints where your customers interact with your business. This includes website visits, social media engagement, purchases, customer support interactions, and more. Understanding the various channels through which customers engage with your brand is crucial for effective integration.

2. Identify Key Data Sources

Pinpoint the diverse sources of customer data within your organization. This could include data from sales, marketing, customer support, and other departments. Recognize the systems and platforms that store valuable customer information. This step lays the foundation for consolidating data and creating a centralized source for all your data.

3. Break Down Data Silos

Overcoming data silos is a critical aspect of integrated customer experiences. Ensure that your customer experience management platform facilitates the exchange of data across departments, eradicating barriers that impede a unified customer view. Collaboration between teams becomes more effective when everyone has access to a comprehensive customer profile.

While this is only a list to get you started and not a comprehensive guide on how to implement integrated CX, your business can still establish a robust foundation for integrated customer experiences. 

How to Measure the Success of Integrated CX

Integrated customer experience can add immense value to your business, but what that will look like will vary from business to business. In order to measure the success of any CX program, you need to understand exactly what you are trying to achieve. To do this, it is important to define your main goals and, more importantly, your main metrics. Here are some common customer experience KPIs and metrics to measure the ROI of Integrated CX. 

Customer Satisfaction Score (CSAT)

CSAT scores remain a fundamental metric for evaluating customer satisfaction. This is most commonly done with a short survey where a customer is asked how satisfied they were with a recent transaction. This may look something like periodically gathering feedback from customers regarding their experiences after the integration implementation. Analyze the CSAT scores to identify trends and areas that may need improvement, providing valuable insights into overall customer satisfaction.

Net Promoter Score (NPS)

Net Promoter Score (NPS) measures the likelihood of customers recommending your business to others. This is most likely done in a survey form by asking customers to answer this question on a scale of 1-10. By tracking changes in NPS before and after implementation, you can assess the impact on customer loyalty. A positive shift in NPS indicates that integrated efforts are resonating positively with your customer base.

Customer Effort Score (CES)

The Customer Effort Score asks the customer how much effort was required to handle a request. Answers typically range from “Very Easy” to “Very Difficult” and are often measured on a scale of 1-5. Tracking these scores and their progressions over time can help you gauge the effectiveness of customer experience initiatives. 

How to Find the Right Integrated CX Solution

Selecting the best customer experience management software for your business may seem like a daunting task, but if you are well prepared then it’ll be a painless process. 

The right customer experience management software for your business will be the one you can partner with. Choose an organization that will take the time to understand your business, your team, and your goals. In order to do this, there are a number of questions you can ask in the evaluation process to find the perfect match. Some of these may be:

• Who specifically will provide implementation and strategic consulting services?
• Which customers can we speak to about your services?
• Will we be charged for survey responses? 

There are more questions to ask and more steps to the evaluation than that, but that is a great start. Other steps may be looking at third-party evaluations such as the Gartner CX Magic Quadrant. 

Learn More About Integrated Customer Experience

For a deeper understanding of the benefits and intricacies of integrated CX, explore our comprehensive resources. Discover how integrated customer experience strategies can drive sustainable business growth and customer satisfaction. You can also dive into case studies, whitepapers, and expert insights to gain valuable knowledge on how to leverage this cutting-edge approach to enhance your bottom line. Learn how you can uncover the power of integrated CX and transform your business into a data-driven, customer-centric success story!

Schedule a demo today to see what InMoment can do for your business! 

In the rapidly changing consumer market, the highest customer-rated Integrated CX company, InMoment, took a bold and proactive approach. On Thursday, January 25th, we hosted the “Changing The Game” event in Austin, TX—a crucible for innovative ideas and game-changing strategies to address the consumer market in 2024 and beyond. 

The event brought in senior leadership from analytics, CX, insights, and VoC programs  from 44 different brands, all with the common goal: sharing how integrated CX is making groundbreaking changes to their companies, customer experiences, and the market as a whole. 

Dive in with us as we share key takeaways from our panels—Integrated CX: Listening Differently, ROI: Measuring Success Beyond NPS, and Reimagining CX with AI— and see how some of the biggest brands in retail, auto, hospitality, B2B, and consumer goods are reshaping their interactions with customers and setting new standards in their respective industries.

Major Hospitality Conglomerate Masters The Art of Engagement

At the heart of one of the biggest dining, entertainment, and hospitality conglomerates in the nation lies a balanced approach to customer feedback: they’ve mastered the art of both reactive and proactive engagement. 

What’s particularly remarkable is that their expansive operation of over 600 locations and reviews is managed by a lean team of two.

By responding to over 95% of negative reviews, their company guarantees that customer concerns are not just heard but addressed; the art of engaged, active listening. This reactive approach is complemented by their proactive strategy of acknowledging and responding to positive feedback—something that businesses often overlook. 

Responding to both positive and negative feedback can’t be overstated enough. Studies have shown that companies that engage with customer feedback can see up to a 5-10% increase in customer retention rates. It lets customers know that there’s a human behind your brand, and you’re open and receptive to changing your processes if inefficiencies are continually being called to attention. 

Engaging with positive and negative feedback consistently, across a vast number of locations, shows that scalability is possible with a small team if the approach is thoughtful and customer-centric.

Worldwide Furniture Retailer Redefines Comfort through Integrated CX

One of the biggest companies in the furniture space is pioneering a future where relaxation meets technology. They’re reimagining  ‘dad’s recliner’ into a modern-day relaxation experience. By merging AI with their products, they’re crafting personalized experiences that evolve with the user. 

McKinsey’s Global Survey on artificial intelligence has reported that businesses adopting AI can see a significant improvement in their performance, with some sectors witnessing profit increases of up to 20% attributable directly to AI. This isn’t just in industries like tech or finserv—this tech is breaching every market. 

Their story exemplifies that the utilization of AI can keep any business competitive and relevant by modernizing their approach. Companies like theirs can meet current consumer expectations while anticipating future needs, securing a leading position in innovation and customer satisfaction.

Major Tech Company Takes a New Angle on NPS Scores

One of the biggest, multinational tech companies in the world is utilizing every available feedback signal—reviews, call center transcripts, social media, and many more—to benchmark against the competition as well as to ensure a balanced view of feedback and signals across different areas of their own company. 

A Harvard Business Review study found that a 12-point increase in NPS leads to a doubling of a company’s growth rate. This approach, especially when applied alongside other feedback mechanisms, offers a comprehensive view of customer and employee satisfaction, enabling businesses to fine-tune their offerings and internal culture. This holistic understanding of feedback signals ensures companies stay ahead in competitive industries by maintaining a pulse on both customer loyalty and operational efficiency.

Through their comprehensive analysis of various feedback signals, they’ve created and maintained a distinct differentiation in their CX program apart from the competitors in their industry. Their detailed assessment offers deeper insights into customer loyalty and competitive positioning in the tech industry, ensuring that they have a complete and actionable view of both their business and the market as a whole. 

Top Car Manufacturer Provides a Human Touch in the Digital Age

One of the leading car manufacturers in the world spoke on a compelling narrative of human-centric customer experience. 

Amidst the digital transformation, they emphasized personal touches, like writing thank-you notes to employees. Emphasizing the employee-customer link has done wonders for their business, where appreciated employees are more engaged and happy at their jobs, which ultimately affects their customer interactions. 

The ability to offer personalized experiences is a critical differentiator for brands, particularly those where customers are dealing with high-cost, heavily-considered purchases such as automobiles and luxury goods. Personalization goes beyond mere customization of products or services; it encompasses understanding and anticipating the unique needs and desires of each customer, forging a deeper connection. 

A study by Epsilon found that 80% of consumers are more likely to make a purchase from a brand that provides personalized experiences. This statistic underscores the importance of a human touch in creating meaningful customer interactions. Brands that master the art of personalization significantly enhance loyalty and a competitive edge. In the context of high-value transactions, it can be the deciding factor that tilts the scales in a brand’s favor, with a higher probability of both a sale and a lifelong customer.

Mobility Industry Expert Reshapes Customer Experience

The journey of one of the big three in the car rental space evolved from traditional survey methods to dynamic feedback systems. Their focus on swift personalization reflects a deep understanding of modern consumer desires. 

A study by Salesforce revealed that 76% of customers expect companies to understand their needs and expectations. In this context, their company’s strategy reflects an acute awareness of modern consumer demands, emphasizing the importance of agile, responsive customer service frameworks that cater to individual needs.

They  prioritized employee well-being alongside customer satisfaction, emphasizing the importance of nurturing a growth-centric ecosystem. Their transformation signifies a broader shift in their identity, from a holdings company to a mobility company, attuned to the nuanced needs of today’s consumer.

Energy Leader Finds AI at the Forefront of Customer Service

A key player in the U.S. energy sector focused on how AI is revolutionizing how call center operations are managed and optimized, particularly in the processing and comprehension of call transcripts. 

InMoment’s toolkit excels in extracting meaningful data from these transcripts, which are often unstructured and varied. The algorithms sift through the data, identifying key themes, customer sentiments, and specific queries, unifying them in a holistic view. This process involves not just transcribing words, but also understanding the context and nuances of each conversation. 

Going a step further, their  AI-powered predictive analytics have been at the forefront of interpreting and extrapolating on data in real-time, helping brands gain a competitive advantage in the ever-evolving marketplace. 

It goes beyond data analysis by setting the foundation where decisions are anticipated, and deeper understanding of current trends helps mitigate future risk. The strategic use of this technology to inform decision-making processes is an advanced approach to business strategy.

As a result, what was once a simple record of customer interactions becomes a rich source of insights, allowing for a deeper understanding of customer needs and experiences.

With AI assisting in call center operations and making systems more optimized, they’re not just retaining loyal customers but also setting a new standard in an industry with historically poor customer feedback mechanisms. Their approach reflects a deep understanding of the need for efficient and personalized customer interactions in the coming decade.

Footwear Retailer Utilizes Unstructured Data as a Training Tool

One of the largest sneaker retailers in the country was one of the earliest adopters of Spotlight, by InMoment. This AI-powered CX software captures and analyzes all customer feedback signals to deliver stronger, more actionable customer experience insights. Like the company mentioned in the previous section, this retailer is also using this tool to pull unstructured data from call transcripts, and taking strategic actions based off of the analyzed data. 

According to IBM, 90% of all data generated by devices such as smartphones, tablets, and connected vehicles is unstructured. The untapped potential of unstructured data is staggering. Leveraging AI to analyze this data, companies can significantly enhance their understanding of customer experiences, leading to more informed decisions and better-aligned strategies.

By using real customer complaints as part of their training, this company ensures that their employees are well-informed and empathetically aligned with customer needs. This approach is a brilliant use of data to enhance customer interactions.

A New Era of Customer Experience

The InMoment (integrated) Experience | Changing The Game was more than a gathering of industry leaders; it was a showcase of the future of customer experience. Each brand, in its unique way, demonstrated that innovation, whether through technology, human touch, or the combination of both, is key to staying relevant and creating customer experiences that drive loyalty. 

As these brands continue to push the boundaries, they’re not only changing the game for themselves, but setting new and revolutionary standards for consumers. It marks the dawn of an era where innovation transcends tradition, compelling the entire industry to follow suit or risk obsolescence. They’re not just leading the change–they’re becoming the change—inspiring a future where excellence in CX is not an aspiration but a given.

Ready to revolutionize your customer experience strategy? Discover how InMoment’s integrated CX solutions can empower your business to listen differently, measure success beyond traditional metrics, and reimagine customer interactions with the power of AI.

Schedule a Demo Today

Did you know that 92% of CEOs agree that customer experience (CX) improvements have a direct impact on their bottom line? It’s clear that a customer experience program is no longer a luxury, but a necessity. 

The process of choosing the best customer experience management software can be tricky and extensive, so there are some things you need to keep in mind as to find the perfect CX partner for your business. 

Benefits of Customer Experience Management Software

Delivering consistent, memorable experiences is no longer a luxury, it’s a necessity. Consumers are more likely to become repeat customers if they have great experiences. But, in order to achieve this, you need an actionable customer experience strategy. That is where customer experience management software comes in. Customer experience management (CXM) software offers several benefits for businesses aiming to enhance their customer interactions and satisfaction such as:

• Improved Customer Satisfaction: CXM software helps businesses understand customer needs, preferences, and expectations. By addressing these effectively, businesses can enhance overall customer satisfaction.
• Enhanced Customer Loyalty: By consistently providing positive experiences, CXM software contributes to building customer loyalty. Satisfied customers are more likely to become repeat customers and advocates for the brand.
• Reduce Customer Churn: According to research by PwC, customers are willing to pay up to 16% more for products or services from companies that offer a better customer experience. Additionally, research by Temkin Group indicates that companies that excel at customer experience have a 16.9% advantage in customer retention rates over companies that provide a poor experience.
• Personalized Interactions: CXM software allows businesses to collect and analyze customer data, enabling personalized interactions. Personalization enhances the customer experience by delivering relevant content, recommendations, and offers.
• Brand Differentiation: Providing an exceptional customer experience through CXM software can set a business apart from competitors. Positive experiences contribute to a positive image, and improved brand reputation management, which aids in differentiating your brand in the market.
• Employee Engagement: Happy and engaged employees are more likely to provide better customer service. CXM software can also contribute to employee satisfaction by providing tools and insights to enhance their ability to serve customers effectively.

How to Choose the Best Customer Experience Management Platform

Choosing the right customer experience management is a critical decision for businesses aiming to elevate their customer interactions. A robust CXM platform can significantly impact customer satisfaction, loyalty, and overall business success. But, choosing the right partner for your business is a complex process. In order to ensure you choose the right vendor, there are some preliminary steps you need to take. 

Look at Third-Party Evaluations

When evaluating CXM platforms, it’s essential to consider third-party evaluations and industry reviews. Independent research firms like Forrester and Gartner provide assessments, such as the Gartner CX Magic Quadrant, that provide valuable insights into the strengths and weaknesses of different platforms, helping you make an informed decision. Look for reviews from reputable sources, industry analysts, and customer experience experts. Assessments often highlight features, scalability, integration capabilities, and overall performance. By leveraging third-party evaluations, you can gain a well-rounded perspective on the platforms you’re considering, ensuring that your choice aligns with industry standards and best practices.

Look at Customer References

Another crucial aspect of selecting a CXM platform is examining customer references. Real-world experiences from businesses similar to yours can offer unparalleled insights into the platform’s practicality and effectiveness. Focus on understanding how the platform addressed their specific needs, the level of support provided, and any challenges faced during implementation. Customer references provide a firsthand account of the platform’s performance in diverse business environments, aiding you in making a decision that aligns with your unique requirements. 

For example, if you are looking for an example of how a customer experience platform helped a large organization put loads of data into one place, look no further than Foot Locker. Foot Locker utilized InMoment’s AI technology to gather data into one place, and sort by sentiment so that customers with negative experiences could be contacted and prevented from churning. 

Look at Their Integrated CX Offering

When considering a CXM platform, it is important to choose a partner that will allow you to do more than one thing. You don’t want a partner who can only do surveys or contact center optimization, you want a partner who will give you an end-to-end look into the customer journey. 

That is why Integrated CX is so important. Integrated CX allows you to bring in data from multiple sources into one central location. From there, you can uncover holistic insights that lead to data-driven decisions. 

10 Questions to Ask CX Companies

When in discussions with your top CX companies, it is important that you delve deeper into their specific product offerings and understand how they go about supporting their customers. You want to ensure that you have a dedicated partner that will help you reach your goals, not just a platform that you will be left in the dark with. In order to do so, make sure you ask questions that will allow you to make an informed decision on a vendor that will work best for you. 

1. What Percentage of Your Total Customer Base Relies on You for Enterprise CX Programs?

When you’re looking for a partner in business, you want them to be an expert in their field. This holds true for customer experience, yet some major companies only dedicate a small percentage of their resources to CX expertise. For example, some major companies claim to specialize in CX, but really the vast majority of their business is devoted to market research. For great customer experience, pick a vendor that is 100% dedicated and will not be distracted by other ventures.

2. What Percentage of Those Customers Have Been With You for Over Three Years?

Some vendors will tout big numbers of clients, but the information that really matters is how long those clients have been with the company. With a strong partner, you get what you were promised and clients are more likely to stick with them longer. Get past the smoke and mirrors and find the right vendor by asking about client longevity.

3. How Many of Those Customers Exceed 1 Million Interactions with You?

If you’re an enterprise, you want to differentiate those who say they can handle a large program with over a million responses and those who are just running a small research survey at

a big company. So how do you tell? Some companies will charge extra with “custom pricing” for responses over 1 million, which highlights their high cost of business and limited experience. You want a partner who doesn’t blink at 1 million.

4. Who Specifically Will Provide Implementation and Strategic Consulting Services?

Continuing the point from the previous question, it’s one thing to claim to be collaborative, but another to have a blueprint for partnership. Ask who specifically will be helping you implement your technology and help you map out your CX strategy to pick out the vendors who walk the walk, not just talk the talk.

5. How Often Will Those Resources Be Available to Us? At What Rates?

Strategy sessions and check-ins are vital to a healthy partnership with your CX vendor. Though they’re vital, many vendors charge extra for the bare minimum amount of sessions. It’s best to clarify that these partnership best practices are included in your contract, rather than an add-on that will cost you more than a pretty penny.

6. Will We Be Charged for Survey Responses? Why?

Some major vendors in the CX industry do not charge you as you’d expect. They don’t charge you based on the number of surveys you send or other elements, but by the number of survey responses you get. If you’re thinking this seems backwards you’d be right, especially seeing as the number of survey responses you’ll get is difficult to estimate going into a contract.

7. What Happens if We Over or Underestimate Our Responses? 

When you sign a contract with a vendor who charges based on the number of survey responses, there is a high probability that you will overestimate and therefore pay more money for services you don’t need. However, these companies do not offer any refunds; in fact they charge steeply if you overestimate. Weed these vendors out to make sure you aren’t backed into a very expensive corner.

8. Are We Subject to Any Parent Company’s Policies and Contracts?

This question is especially relevant due to recent acquisitions across the CX landscape. Now more than ever, it’s important to know if you’re partnering with just the technology vendor or if you’re signing something that makes you beholden to a parent company’s interests and policies. Ask this question to clarify if your vendor is working for you or for their parent company.

9. Can We Review the 24-month Product Roadmap?

Crafting a roadmap for your initiatives is necessary to not only get the quick wins you need, but to set long-term goals. However, not even CX professionals can see the future. There will be unexpected events that may necessitate adjustments to your roadmap, yet some vendors don’t allow tweaks to the plan. Clarify this with your vendor to make sure your program is future-proofed.

10. Which Customers Can We Speak to Verify Your Responses?

Strong partners create strong advocates. It’s as simple as that. Ask prospective vendors if you can speak to current customers and the best of them will refer you to an advocate that will be more than happy to tell you about their experience.

How Customer Experience Management Started

At its simplest, customer experience management is a broad term that refers to evaluating and managing a customer’s every interaction with a brand. Though many companies have taken strides to provide great customer experiences for many years now, the idea of customer experience management as its own science or discipline really didn’t come about until the early 2000s. 

That’s about when advancements in technology allowed customer experience to go from being an abstract goal to something more quantifiable. Suddenly, companies everywhere could use the internet to track site visits and other metrics, opening up a whole new dimension to the idea of caring for customers. If these elements were quantifiable, that meant they could be managed. And if they could be managed, then perhaps they could be meaningfully improved to create a bolder, more human, and more invested relationship with every customer!

Though today’s conversation focuses on customer experience management, it’s important to remember that this technology and science doesn’t ‘just’ apply to customers. Many brands also use tune experience management tools to their employee experiences. The idea with this approach is to create a better workplace culture, reduce employee churn, and create the same kinds of fundamental relationships with workers that brands aspire to build with customers.

Customer Experience Management’s Early Days

Now that they were armed with the technology needed to evaluate a lot of customer experiences in little time, companies turned their attention to the next frontier of feedback collection: digital surveys. Surveys had, of course, been around for a long time, but mailing them out or publicly soliciting customers to take them on the spot was expensive and produced inconsistent results. 

Suddenly, though, these companies had access to newly developed survey deployment technologies and, before too long, tools that allowed them to build their own questionnaires. Both approaches, combined with email, suddenly made sending massive numbers of surveys directly to customers much simpler and much more cost-effective. Surveys thus became a cornerstone of customer experience management, a role they still have to this day!

Customer Experience Management’s Continued Evolution

With these new survey tools, methods, and partnerships in hand, brands rolled up their sleeves and got creative in the pursuit of feedback. Whether it was promising a free soda upon survey completion or a discount the next time customers came in, countless organizations spent the 2000s attempting to gather as much feedback as possible. 

At this point, the terms “customer experience” and “customer experience management” weren’t as ubiquitous as they are now. Rather, a lot of organizations and the vendors that provided survey tools used phrases like “brand protection” to describe why it was important to adopt an approach like this. Over time, though, the term “customer experience” became a mainstay of this discipline, and terms like “customer experience management” soon followed. Because of the employee experience approach we mentioned earlier, it’s common nowadays for this science to be referred to simply as experience management (XM).

The Rise of Big Data Within Customer Experience Management

Once organizations got their feet wet building surveys, analyzing data, and figuring out how to incentivize customers and employees to respond, they had to take the next step in the customer experience management journey: making sense of feedback. No small task, especially when the field was in its infancy, but both brands and experience vendors were determined to make sense of all the feedback they were receiving.

This was about the point that the term “big data” entered the experience conversation, and it became a bylaw of experience programs throughout the late 2000s and early 2010s. Having a ton of data was suddenly all the rage, and organizations spent a great deal of time and money gathering mountains of it in pursuit of better customer experience management. Frankly, there was no tech or business problem that a lot of brands thought they couldn’t solve just by throwing data at it.

However, this is the part of the story where the customer experience management revolution ended up stalling out for a lot of brands. They’d gathered lots of data, yes, but what a lot of these brands and the vendors that partnered with them didn’t quite grasp at the time is that big data alone cannot solve your business and customer experience problems. Nonetheless, big data remained the north star of many experience programs, which, frankly, is why a large number of them failed.

Customer Experience Management Hits a Plateau

After it became apparent that simply gathering data and feedback from surveys didn’t bridge the gap to actually fixing problems, the next step for customer experience management vendors and their clients was figuring out how to, well, fix problems. These brands had business challenges, and they had big data. What did building a connection between the two end up looking like?

The truth is that, whether back in the day or right now, a lot of organizations still haven’t quite figured that out. You might say that brands should simply take a look at their data and infer solutions from there, but for many companies, their big data is literally too big to make that idea feasible. There’s simply too much noise and no easy way to find signals in it. Or at least… that was the case until relatively recently.

Going Beyond Customer Experience Management 

Until the last few years, one of the biggest bywords of customer experience management was basically to gather as much data as possible and hope that brands could use it to adequately react to customer and employee complaints after the fact. This philosophy played out in the form of customer experience teams who kept their data siloed or vendors who offered entirely reaction-based and DIY solutions without much customizability or human expertise.

At InMoment, however, we believe that the experience management story should be an Experience Improvement (XI) revolution. As you’ve seen by now, while data and metrics are certainly very important, just having a large pile of them doesn’t actually translate to solutions for business challenges, customer relationships, employee retention, or countless other experience factors. Successful customer experience management demands much more.

From Customer Experience Management to Experience Improvement

There are a few factors to bear in mind for making a difference with your customer experience management. The first is to remember that truly great experience management doesn’t start with gathering data; it starts with figuring out which tangible business goals you need your program to accomplish. We call this designing with the end in mind, and it’s a strategy that will make your data so much more manageable than older approaches aimed at gathering as much of it as possible.

This strategy will also result in much more relevant customer sentiment, which is key to understanding what they love (and don’t love) about their experiences with your brand. You can then apply this heightened understanding toward meaningful transformations within your business and its associated customer journeys, realizing that success in the form of those goals we mentioned earlier (retention, acquisition, saving costs, etc.).

That idea of being selective with your data, as well as proactively sharing the data you do gather, feeds directly into the very best elements that effective customer experience management and Experience Improvement have to offer. More accurate personas, better defined marketing segments, better touchpoint evaluation, and knowing what your customers want before they themselves do are but a few perks to this approach.

InMoment as a CX Partner 

InMoment has best-in-class NLP capabilities and has the highest user ratings of all Voice of the Customer companies according to Gartner Peer Insights. Schedule a demo today to see what we can do for your business! 

REPORT

InMoment Named a Leader in The Forrester Wave™: Text Mining And Analytics Platforms, Q2 2024

InMoment’s platform and personnel have been recognized by Forrester as “major players in the text mining and analytics market.” With a platform proficient in text mining as a way to pull insights from customer feedback, and a team determined to add value and improve business performance, InMoment is leading the charge in the text mining and analytics industry.

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