3 steps to exceptional CX using automated predictive analytics (Infographic)
Dan Somers, CEO at Warwick Analytics, presents three steps to achieving customer delight with automated predictive analytics
According to a recent Gartner study, the vast majority of companies surveyed (89%) believe that the customer experience is the new area of focus for 2016. However, B2B organisations are struggling to define and implement customer service approaches that meet their clients’ evolving needs.
Whether you are using NPS, CSAT, NetEasy Score or CES, you have customer satisfaction KPIs in place. But how do you know that these measures are useful predictors of behaviour?
Most marketers and customer services managers accept that customer satisfaction KPIs focus on historic customer feedback with fixed measures and outcomes, and are actively provided by the customer which may be self-serving and unreliable. Customers in the “neutral” and “dissatisfied” categories often don’t fill out surveys, making the potential for skewed results high.
Yet what else are B2B marketers supposed to do? It is not seemingly obvious how else to measure the success of customer services particularly where there is a departmental separation between customer services, marketing and sales.
Welcome to customer satisfaction 2.0
By introducing predictive analytics, you can turn this concept on its head and ask ‘what are the outcomes you are actually looking for?’ Maybe you want to know about a very specific target group or how a specific issue might directly affect sales, now or in the future, or to come up with a dynamically generated holistic KPI which best predicts organic sales and loyalty.
Predictive analytics helps you answer these questions and more. It’s ultimately about optimising and prioritising the actions to do with customer satisfaction so that they directly influence organic sales i.e. RFV and/or WOM.
So what are the steps involved? There are three steps which can be undertaken by data scientists, or even better with automated predictive analytics:
Step 1: Simplify
The first step is to cluster your data, such as the Voice of Customer (“VoC”) data, so that semantically similar reviews and comments are aggregated. These data can be social media, reviews, weblogs (if e-commerce), loyalty data, CRM, POS, Customer Satisfaction Surveys etc. Similarly, customers can be clustered by attributes and behaviour. This provides value in itself in terms of insight as well as providing the foundations for further analyses. Note that there are nuances in terms of complaints and reviews which are from positively or negatively motivated customers. In other words, they may not necessarily be representative. Segment normalisation analysis is required to give a more accurate picture. Further, if a business is multichannel then the reviews might only come from the e-commerce sales, and need to be normalised too.
There are different techniques for clustering depending on the data and the data scientists’ preferences. We mentioned automated predictive analytics briefly above. This is a new breed of predictive analytics which does not require pre-defining terms or feature extraction. It can be of great assistance in this step, as well as automating the subsequent steps to get to a usable answer quickly, in a matter of minutes rather than weeks (data scientists spend c. 80% of their time manually transforming, cleansing and preparing data). Dictionaries are an output to be validated rather than an a priori input.
Step 2: Predict
Once the data are simplified and aggregated, there are a variety of statistical and machine learning techniques which can take the results from Step 1 and fast forward them to see how a specific issue or sales pattern will look at any fixed point of time in the future. Which issues are the most significant now or growing? How will they affect organic sales? Is a specific problem affecting a specific customer segment?
Again, automated predictive analytics can greatly help here: All of the predictive model building and validation occur in an automated ‘workflow’ which learns and improves as new data arrive. A reliable forecast of issues and their effect on organic sales can be generated.
Step 3: Recommend
Many commentators refer to ‘prescriptive analytics’ as insight which is actionable. This can take various forms such as a traffic light indicator e.g. of a customer likely to churn, or a recommendation to do a specific action such as a pre-emptive outbound customer care call. These in turn might be sets of business rules or ‘root cause’ e.g. if a customer has complained then it is 50% more likely to leave if there’s a further poor experience in the future.
So many times businesses try to solve known issues in the wrong area or starting point. Often the root cause is quite removed from the symptomatic pain points and only with complex automated predictive analytics can this be clearly identified and resolved.
By pulling together all these three steps and finishing with a set of prescriptive analytical results, it is easy to see how Customer Satisfaction 2.0 can enhance customer services. Now there is potentially a prescriptive action with a quantified percentage effect on organic sales for every corrective and preventative action that customer services can take either towards individual customers or customer segments. Actions can be prioritised on predictive behaviour and the ‘closed feedback loop’ between sales and customer services can be achieved. Whilst this can be achieved stepwise with a team of data scientists and the state-of-the-art tools, it can be shown that automated predictive analytics can quickly arrive at Step 3 and dynamically update as new data arrives.
So, we can see that by first aggregating the data in a normalised way, then predicting outcomes and finally making clear recommendations for improvement, new KPIs are generated. These can still be used to remunerate and monitor managers, albeit more aligning the outcomes of the company without reengineering the business. Also, by further understanding the link between customer satisfaction and organic sales (or RFV) then the costs of the customer services department can be optimised and not overspent on non-priority actions.
Automating predictive analytics can enable staff to work smarter and faster, not handing over to the analytics team to spend 80% of their time preparing the data.
CXcellence: How to achieve CX success in B2B
When does customer experience matter most? And what can you do to deliver an exceptional customer experience throughout the customer journey?
In this research project we surveyed 100 B2B buyers and 165 B2B marketers to find out exactly when CX matters most, and which parts of the experience marketers should be focusing their efforts on.
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- Which CX factors are the most influential in buyers' decision-making
- Where marketers are currently focusing their CX efforts, and their biggest CX management challenges.
Read this report to:
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