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4 min read
Pawel Laskowski

With insightful customer data at their disposal, retail brands are in an advantageous position to lean on analytics to anticipate customers’ needs and act on them accordingly. However, access to these metrics is by no means guaranteed.


According to one study, 80% of retail respondents understand the value of a customer relationship management (CRM) system that provides a single source of information – yet, as of this year, only 25% of brands actually have one.


The ability to anticipate customers’ needs can’t be overstated. It is the heartbeat of a positive customer experience which minimises customer churn that routinely outpaces the competition. Investing in and leveraging predictive analytics can build more proactive customer insights – but analytics are best used according to what’s in your brand’s best interest.


How predictive analytics can prompt retailers to be proactive


The cost of finding customers versus keeping current ones is well-documented. Research shows that customer acquisition can cost up to five times more than customer retention. Knowing what drives the people who leave your brand for another can be inexact; predictive analytics uses the numbers to tell those stories.


With data mining, statistics, model building, artificial intelligence and machine learning, predictive analytics can identify those on-the-fence customers and help retail brands be more anticipatory. Rather than be caught off guard by high churn numbers and decreasing bottom lines, companies can use predictive analytics to continue modifying their approaches and providing customised experiences that keep buyers engaged.


And it’s that front-end analysis that can lead to back-end benefits. Research by HelpLama says 89% of customers think proactiveness is integral to a positive buyer experience, while 81% of those surveyed by Zendesk say a positive experience inspires them to buy from a brand again.


Predictive analytics access can provide retail brands with an updated view of what can lead to customer churn and how best to get out in front of it. Seeing these trends can empower brands to see customer retention from a 360-degree perspective and remain steps ahead in terms of churn.


Incorporate predictive analytics to reduce customer churn


An effective predictive analytics strategy doesn’t just apply numbers to a customer churn challenge. It assesses organisations from every angle to compile the metrics that will best solve their retention issues. To implement a predictive analytics strategy that helps your retail brand get ahead of potential churn, prioritise these four strategies:


Rely on clean and accurate data 


Predictive analytics doesn’t have the intended impact if the data doesn’t have a transparent and high-quality foundation. Before applying these findings to any customer churn mitigation strategies, make sure the data being used is beyond reproach. 


It’s critical to take any measures necessary to polish data and fill in any gaps that might exist within the data collection. Make sure the integrity of the data is never in question so that whatever actions this analytics sparks are grounded in fact and engage customers correctly.


Pick the right model


Predictive analytics can outfit retail brands with empirical insights that guide your customer engagement tactics. But it’s not a one-size-fits-all practice. Choose a model that best lends itself to the business model, data and use case at hand.


  • Logistic regression: This binary model can forecast how likely customer churn is for a particular customer and help customer success managers personalise their engagement tactics. Because of its simplicity, the model can yield inaccurate results, which is why it’s sometimes complemented by another model.
  • Decision trees: These are model decision-making processes that work to understand segments of people based on their behaviours and preferences. Best used in conjunction with other algorithms, a decision tree can highlight areas that are more or less likely to churn.
  • Random forest: This combination of several decision trees can provide a clearer, more all-encompassing churn prediction. Random forest models typically need more time and testing to find those results.
  • Neural networks: These models are geared toward businesses with large data sets containing complex, non-linear relationships. Neural networks use sophisticated computational resources to go beyond if or when customer churn occurs to better understand why it does.
  • Survival analysis: This model can measure if churn will even occur. Survival analysis is best applied when breaking down customer lifelines or demonstrating when to execute any time-sensitive retention tactics.


Predictive models can take findings and give them a semblance of structure. The actual metrics and models can come together to make brands more informed and better equipped to solve their most pressing customer churn challenges.


Acquire the necessary data expertise


Once collected, predictive data must be leveraged. Whether that means using, interpreting or sharing it, a certain level of knowledge is necessary to maximise predictive analytics’ limitless potential.


Build a team equipped to understand all the value afforded by these data points. That can be an in-house team of experts, or it can come from an outside partner who supplements your staff to fill expertise gaps. All that matters is having an experienced group of specialists in place to make sure the data is used as intended.


Make privacy a core value


Per a dotdigital survey, 67% of retail consumers believe the data they share will be hacked. With security always a chief concern among shoppers, be diligent about protecting customer data.


Try to stay in the know about local data regulations to be sure the data is being collected ethically and lawfully. This gives consumers confidence in your brand and ensures engagement strategies can occur without disruption.


Churn is a natural part of the business ecosystem. But it’s up to retailers to curb customer turnover and build a pipeline of engaged, repeat customers. Predictive analytics can eye potential churn issues ahead of time so companies can keep churn to a minimum and build a reputation as being more proactive and people-focused.


Predictive analytics has a wealth of benefits and applications for engaging customers. Learn more about those advantages by diving into Mastering Customer Retention in Retail, our new co-branded e-book with Google Cloud.


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