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Successful brands use data to direct business strategy, optimise customer journeys and craft relevant content to build long-term relationships. The beauty of loyalty is it’s the most influential mechanism for capturing customer data while operating within data privacy regulations because the fundamental principle of loyalty is a customer willingly volunteers their data in exchange for benefits. As they opt-in to the terms of the program, marketers can serve up offers and content that helps emotionally bond the customer to the brand. However, many brands are struggling to pull insights from their data from which to build the strategy and campaigns to make their data come to life for them.

To start their data and insights strategy, marketers should consider their primary goals, likely personalisation, comprehensive view of customer, customer growth, churn reduction, etc., from which to establish KPIs that align with business needs. These KPIs go beyond loyalty program mechanics and metrics to affect all business units, from operations carrying out customer experience to merchandising determining the products to feature to marketing as a whole in making acquisition more cost-effective.

The point to remember is that the goal of both technology and strategies is to make data useful. This may require that some data is pared away or left unused. As such, the platforms and strategies that brands choose to adopt should focus on structuring data into unified, accessible views that will enable the team to design successful strategies.  

Using the right analytic tools can help combat data overwhelm and enable marketers to interpret data efficiently to craft the campaigns that influence customer behavior. Some examples include:
 

  • Sophisticated Segmentation – allows you to cluster customers into key segments with as much granularity as you can muster based on data collected through your platform. This allows marketers to trigger personalised, relevant campaigns to members at scale, and avoid blanketed offers that may alienate some members. With one client whose campaigns feature personalised content reflecting the customer’s journey, Aimia has seen a 19% lift in click-to-open rate and a 24% gain in the average volume of unique clicks per send. Additionally, a specialty retailer in the US used advanced behavioral segmentation in combination with the SmartJourneyTM methodology to identify key customer segments and behaviors they had missed before, to influence behavior to a 14% lift in sales in the first 90 days of launch.
 
  • Customer Lifetime Value Modeling (CLTV) – identifies a customer’s value at any point along their customer journey. Here, brands can define who their highest value customer is (not only in terms of spend, but also advocacy and profitability to the brand). Knowing the value of customers allows marketers to build strategies like inspiring customers toward a higher value, predicting who is likely to become a high value customer and sending campaigns to inspire them along their journey, and knowing who the highest value customers are, what behaviors and traits they have in common and comparing that to business objectives can determine how much and how to invest in them, and can even build look alike models to design campaigns to attract new members most likely to become highest value members.
 
  • Predictive Churn Modeling – based on industry benchmarks and historical behavioral data, predictive churn models can indicate who is likely to churn from the brand and when. With this knowledge, the model can automatically trigger re-alignment campaigns to stem churn and bring customers back in line with their brand, potentially preventing millions of dollars in lost revenue opportunity. Churn models enable marketers to spend more wisely and realise a greater ROI; recognising cost savings ranging from marketing spend to deliver campaigns to offer redemptions (e.g. points, discounts, etc.) by customers who don’t necessarily need these incentives to engage. When executing campaigns from broad customer churn strategies versus a predictive churn model, Aimia has seen brands 7 times more likely to present an offer to a customer who doesn’t need it, which equated to an ROI of 11% less.
 
  • Recommendation Engine – can send the next best offer to customers with a high degree of certainty of conversion. The recommendation engine uses segment information as well as identifying any outlying traits the may preclude a member from purchasing what the segment typically could, and crossing that with business goals, to increase upsell and cross-sell opportunities. This high-touch personalisation is instrumental to growth, Aimia has seen 14% incremental lifts in visits and 12% lift in spend. Aimia also sees between a 30% and 380% lift in open rates for push notifications that use personalised targeting or trigger criteria vs. the average “mass” campaign sent to the broader loyalty audience.

 

From where Aimia sees it, tackling the data and analytics challenges can be done through both finely tuned martech and expert resources that bring data to life to create insights that can translate to action. This is an exciting time for brands to differentiate from competition, demonstrate their customer obsession, and continue to grow their customer base and revenue. 

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