Implementing Advanced Analytics throughout Customer Lifecycle (1/3)
The current post is the first part of the three that showcase how advanced analytics can be used throughout the lifecycle of a customer. Due to the length and depth of the whole article, it will be split into three parts, each dedicated on a separate customer lifetime stage, (1) Segmentation (2) Acquisition & Engagement, (3) Retention.
Customer analytics is the subgroup of analytics which is responsible for understanding the needs, behaviours and expectations of customers.
The main objective of customer analytics is to create an accurate and unique profile of each customer. By better understanding each customer’s habits and lifestyle choices, companies can more accurately predict his behaviour and eventually enhance the customer journey by providing better experiences throughout his whole lifecycle.
The importance is also evident from emphasis given from McKinsey Quarterly which stated, “companies that make extensive use of customer analytics are more likely to report outperforming their competitors on key performance metrics, whether profit, sales, sales growth, or return on investment.”
Customer analytics is considered the major pillar on which marketing activities should be based on and by using predictive modelling, data visualisation and customer segmentation techniques, businesses can really enhance the relevance of marketing campaigns and customer experience.
Non technical challenges
Despite the fact that customer-related analytical methods have been around for ages though, it is only recently that plentiful availability of cheap computing power, combined with the user-friendliness of statistical programs and the abundance of libraries of programming languages allowed the introduction of such methods in the business world on a regular basis.
With the vast availability of data gathered in today’s modern organisations, it would be unfathomable and impossible for a human analyst and traditional statistical methods to make sense, let alone provide actionable insights and recommend solutions, without the help of advanced analytics.
However, in order to fully exploit their power, companies and organisations need the following proficiencies:
1) Trustworthy and accurate data, stored in a structured and easily retrievable way.
2) Employees with the capability to understand, design, create, model and utilise the aforementioned analytical methods and tools.
3) Management that is supportive towards the data-driven approach and willing to take actions based on the delivery of the findings.
The last point is very often overlooked, but its importance is critical as even the most advanced analysis with the most accurate data cannot help a business succeed if its findings are not interpreted correctly and communicated effectively in a timely manner.
“Customer analytics is the subgroup of analytics which is responsible for understanding the needs, behaviours and expectations of customers.”
Analytics at every step
While some businesses are already utilising those tools, they either limit their usage to specific use cases and lifecycle steps or do not take full advantage of customer insights available.
It should be noted though that advanced analytics can play a significant role throughout the whole customer lifecycle, from initial acquisition of customers to cross-selling efforts, all the way up to churn prevention and reactivation efforts.

Moreover, advanced analytics methods can help a company move from merely descriptive insights that describe the past “i.e. 20% of our customers bring in the 80% of the revenue”, to proactively safeguard customers who are likely to defect based on predictive analytics and offer up-sell personalised offers exactly when each customer is anticipated that he needs them.
In general, each step throughout the customer lifetime has a methodology available that can be used to achieve a desired outcome which results in increasing revenues and profitability. Those outcomes can be reached by continuous activation of value-generating behaviours.
Customer profiling — Segmentation
Customer segmentation allows companies to divide the customers into natural groupings that share similar characteristics or behaviours.
Segmentation should be used to build a comprehensive view of both potential and existing customers, as all targeting decisions with personalised messages and campaigns thereafter will be based on insights derived from this method.
This becomes possible by the vast customer data that are available in today’s world, ranging from static demographics to daily transactional data, and by the utilisation of the available analytical models and advanced grouping techniques.
It should be emphasised that segmentation is valuable across the whole customer life-cycle, from the initial stages of acquisition and lead generation, all the way up to churn and re-activation phase.
Clustering, which is an unsupervised learning method, meaning it does not need examples and “training” from analysts to provide results, is the most popular technique to group customers into segments. Using this method, customers with similar demographics and lifestyle choices are put automatically in the same group. For example, customers that have expensive spending habits and high frequency in their purchases will be separated from those who did only one purchase several weeks ago.
Once certain customer groups are identified as core targets, it is often advised for classification algorithms to follow, to assign new customers to each of those predetermined groups.
Classification algorithms, such as neural networks or decision trees, can be utilised to determine preferences, behaviours and lifestyle choices of new customers, such as “fitness junkies” or “working young mothers” and put them in the most relevant cluster. This enables companies to promote certain products or services that may be of high interest.

Recommendation engines use methods such as linear regression and nearest neighbours to compare peers and when combined with logistic regression, they could measure the probability of a customer responding to a product offering.
This is particularly valuable for business, as offering a customer an additional product that suits his needs will not only increase sales, but also strengthen customer loyalty and satisfaction.
The second part which presented advanced analytics methods for Customer Acquisition & Engagement can be found here, whereas the third part for Customer Retention can be found here.

