Customer surveys are a conduit to the voice of the customer (VoC). However, simply capturing survey data is no longer enough to achieve better results.
When used appropriately, customer surveys can help companies more effectively identify new markets with the most potential for success, create a data-driven pricing strategy, and gauge customer satisfaction. However, capturing survey data is only the first step.
Companies must analyze and act on survey data to achieve their goals. This is where predictive analytics comes into the picture. As illustrated in Figure 1, companies using predictive analytics to process survey data achieve far superior results across several key performance indicators (KPIs), compared to those without this technology.
Since happy customers are more likely to maintain or increase, their spend with a business, growth in customer lifetime value among predictive analytics users signals improvement in customer satisfaction rates. Similarly, companies using this technology also attain 4.6 times the annual increase in overall sales team attainment of quota, compared to non-users. This correlation indicates that predictive analytics can help companies convert survey data into top-line revenue growth.
Use of predictive analytics to forecast and predict the likelihood of certain events, such as potential sales or changes in customer satisfaction, requires companies to have a comprehensive view of customer and operational data. Most organizations don’t struggle with a lack of survey data given the wealth of insights they glean through the activities noted above. Instead, they are challenged with putting this data to good use. Indeed, findings from Aberdeen’s May 2016 study, CEM Executive's Agenda 2016: Aligning the Business Around the Customer, show that only 15% of companies are fully satisfied with their ability to use survey data in customer experience programs.
How to Use Predictive Analytics to Maximize Your Performance
Data shows that Best-in-Class firms (see sidebar) are 20% more likely to be fully satisfied with their use of survey data when conducting customer conversations. A closer look at these organizations reveals that they have 59% greater adoption rate when it comes to predictive analytics, compared to All Others (35% vs. 22%).
For any organization not currently using predictive analytics to analyze survey data, this technology holds the key to significant performance improvements. As such, we see that with a mere 35% adoption rate, many top performers could use predictive analytics to do even better.
One mistake companies make when adopting new technologies is assuming that simply deploying the technology will result in sudden – and recurring – performance improvements. The
situation is no different with predictive analytics. The fact of the matter is, if an organization is looking to increase customer lifetime value or profit margins, the organization must design and execute a well-crafted strategy for utilizing predictive analytics in conjunction with customer surveys.
On a high level, predictive analytics can be used in two ways:
Systematic analysis: Organizations can establish an analytics program to measure and manage survey data on a regular basis. These programs are aimed at accomplishing certain goals, such as gauging customer satisfaction levels at regular intervals to correlate changes in customer satisfaction rates with changes in the marketplace and overall business activities.
Ad-hoc analysis: Companies can also analyze survey data on an as-needed basis. For example, a company could conduct a one-time analysis of the potential customer spend in a new market to decide whether to enter that market.
It’s important to note that companies can use both systematic and ad-hoc analysis. Use of systematic analysis allows organizations to continuously monitor their progress towards ongoing performance goals, such as improving customer satisfaction. Ad- hoc analysis, on the other hand, allows companies to use the same analytical capabilities to answer specific questions that may arise.
Having outlined the two general ways companies use predictive analytics, it’s also important to share the two general types of processes that can be used to produce such analysis:
1. Statistical analysis: Predictive analytics can provide decision maker across the business with insights into hidden trends and correlations. For example, companies conducting statistical analysis can identify how use of certain customer interaction channels (e.g. web, email, or social media) correlates with customer satisfaction rates as revealed through surveys. This, in turn, allows companies to identify which channels work best in meeting (and exceeding) the needs of target clientele.
2. Modeling: This second type breaks into two sub-categories:
Forecasting: Companies can use historical and real- time survey data to forecast the likelihood of certain outcomes. For example, a company curious about the potential sales uplift to be expected from a new market would survey potential buyers in the area and ask about their intent to buy and preferred price-points. The forecasting capability of their predictive analytics platform would then allow the company to forecast potential sales numbers.
Predicting: This analysis refers to analyzing historical and real-time survey data to estimate a specific result that might have already happened, might happen currently or will happen in the future. For example, an organization might decide to build a model that helps identify customer spend in a specific market. This might start by developing a model for past sales results where the model produces a result similar to the actual results observed by the company. Having ensured the accuracy of the model, the organization can now use it to predict current and future sales based on changes in the factors built into the same predictive model.
The difference between forecasting and predicting is that the former only looks at future events or values whereas the latter can look at future, current or historical events when building models. Also, the former requires relying on already available past data (e.g. snow blower purchases) to make forecasts whereas the latter allows companies to predict a certain outcome, in this case snow blower purchases by looking at related factors influencing this result, including recent temperatures, change in average income, and others.
Companies have many ways to capture survey data, however only 15% are fully satisfied in their ability to use this data. Predictive analytics helps companies alleviate this challenge by answering business questions designed to improve performance results.
However, it’s imperative to remember that the statistical insights gleaned through predictive analytics, as well as the models predictive analytics can produce, will only yield results if companies act on the intelligence thus acquired. Don’t overlook the importance of coupling analysis and action. If you are planning to invest in this technology (or have already invested but seek to improve your results), we recommend that you make bridging the gap between data and action a key priority for your business.
Author: Omer Minkara
Source: white paper Aberdeen Group (sponsored by IBM)