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Customizing marketing strategies keeps brands competitive and customers happy.


SEP 24, 2018

In this age of dominant giant online retailers, aggressive competitors, consumer marketing overload and dubious customer loyalty, retailers and other online brands need to build meaningful, long-term relationships with customers in order to survive and thrive. These consumer-focused strategies require lots of data — which most online businesses already have available — and an intelligent way to find the actionable insights hiding within the data. Read on to discover a number of best practices actually used by successful brands in order to leverage their data to increase customer loyalty, spend and lifetime value.

1. Identifying the most effective offers

An online women’s lingerie retailer identified the exact customer segments that generated the best ROI for each of the following offers: “Buy 1, Get 1 Free,” “Buy 2, Get 1 Free” and “Free Shipping.” Using these insights, the brand has optimized their campaigns to deliver the most effective offer to each individual customer, maximizing both short-term revenues and customer lifetime value.

2. Identifying the most valuable first purchase categories

The same lingerie retailer identified the first-purchase product categories that generate the highest future value for different customer segments. By encouraging customers to make their initial purchases from these categories, along with sending each one the most effective of the aforementioned offers, the brand increased its direct relationship marketing revenue by 15 percent, multiplied its number of active customers by 2.3x and increased average order value by 22 percent.

3. Prioritizing product preference variations

A subscription meal delivery company identified variations in meal category selection within the first 30 days as being strongly predictive of very high customer future value. The company then automated their engagement strategy to encourage customers to diversify their order categories as early as possible.

4. Recognizing the value of product ratings

This meal delivery company also surfaced the insight that customers who rate their meals — regardless of rating level — exhibit dramatically higher future value. The brand has since implemented automated strategies to increase the number of customers who rate their meals. Following this insight among others, the brand’s relationship marketing automation efforts have increased average order value by 64 percent, increased customer LTV by 19 percent and decreased customer churn by 22 percent.

5. Identifying an optimal discount level

An online cosmetics retailer identified a 5 percent discount level as optimal for maximizing spend and customer future value while minimizing revenue cannibalization due to excessive discounting. Interestingly, customers who received the smallest discounts (up to 5 percent) exhibited higher future value than both those who had received high discount levels (10 percent +) and those who had been given no discounts at all.

6. Recognizing first-purchase amount as an indication of future behavior

This cosmetics retailer also identified customer segments comprised of new customers who exhibit a higher likelihood of coming back for a second purchase, based primarily on first-purchase amount. The brand subsequently built a CRM strategy to segment and treat these customers differently, resulting in significant increases in purchase frequency, spend levels and lifetime value.

7. Prioritizing early cross-selling

The same cosmetics retailer surfaced the insight that people purchasing from multiple product categories in their first two orders have significantly higher future value over the long term than customers who purchased from within a single category. This was a counter-intuitive observation, given that short-term customers who made two purchases from the same category initially spent more. This insight demonstrated the importance of early cross-selling efforts for this brand, something they are now leveraging via timely product recommendation campaigns.

8. Using platform preference as an indicator

An online fashion retailer analyzed its customer data to reveal that customers using the retailer’s iOS app spent 76 percent more during their first year, compared with all other customers. This insight has led the brand to now automate a differentiated customer communications strategy based on platform preference.

9. Predicting and preventing churn

The same fashion retailer analyzed purchase patterns among different segments in their customer database. By focusing on individual customers’ purchase frequency among other attributes, they were then able to use this information to build individualized risk-of-churn predictions.. With a much more segmented and accurate understanding of churn probability, the company has succeeded in increasing customer loyalty through personalized churn-prevention campaigns.

10. Utilizing usage to indicate an ideal conversion point

A video streaming platform used customer modeling to surface the point at which subscribers over-use the service, creating a burn-out effect that leads to customer churn. By identifying this inflection point, the brand has been able to target their customers at the exact best time to maximize loyalty and grow their active customer base.

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