Identifying Killer Customer Offers with Ecommerce Analytics

If you are selling products, services or content online, in all probability, you are operating in an extremely competitive environment. With a plethora of brands in every niche, every online store vying for customer attention across channels and a fickle customer base always on the lookout for the best promotions, it is essential to catch the customer’s eye at the right time with the most pertinent offer.

Offer design and delivery can be the single largest capability driving conversions for your ecommerce store if executed on the basis of intelligent analysis of store data.

Following are just a few of the ways, ecommerce analytics can help you design and deliver promotions that are just too hard to resist for your customers!

1. Clickstream Analytics

From the moment a customer lands on your ecommerce store page, it is possible to track each and every click in real-time. Specific actions such as search queries, product views, additions to wish lists, additions to carts, checkouts, payment failures, likes, shares and even bounces can be tracked.

This clickstream data is a gold mine for ecommerce analytics as it can help you understand the customer shopping journey and brand engagement levels across devices in tremendous depth. This data, in combination with dynamic offer design capabilities, can enable you to deliver targeted offers that can drive cross-sell and upsell opportunities as well as combat exit intent.

For example, consider a situation wherein a customer is about to abandon his cart mid-purchase by closing the shopping app or browser. By delivering an instant notification of a limited period discount, it is possible to retain the customer and entice him to complete the purchase.

Clickstream Analytics also allows you to identify the chief sources of customer traffic – among search, social, email, display, retargeting and others – which can then be leveraged for high converting offer placements.

2. Market Basket Analytics

By analyzing electronic order data, it is possible to determine products that the customer is likely to buy with another group of items. It is also possible to identify the Next Best Product (NBP) and Next Best Offer (NBO) recommendations and track the repeatability/ cyclical nature of a customer’s shopping behavior over time to deliver suitable offers.

For example, suggesting the right accessories for a recently bought mobile phone or offering discounts on related app subscriptions can dramatically increase the probability of conversions. World ecommerce leaders such as Amazon and Netflix track and analyze the following data for generating NBP and NBO recommendations,

  • Purchasing history
  • Past ratings and review history
  • Items in customer wish lists/ watch lists
  • Purchasing patterns by similar customers

3. Location Analytics

With more and more customers shopping on mobile devices, customer location data is now easily available and can be leveraged to generate localized product recommendations and offers. It is possible to identify patterns correlating the location of customers with shopping preferences and deliver customized offers, especially if your ecommerce store operates in conjunction with offline stores.

It is also possible to design specific shipping related discount offers based on inventory and customer location data.

4. Customer Analytics

Integration of CRM data (Customer relationship management) into the overall ecommerce analytics strategy can help you segment your customer base and design targeted offers accordingly. A wide spectrum of customer data including demographics, payment preferences, shipping preferences and loyalty levels can be used for creative offer design.

For example, for SaaS providers, it is possible to segment customers based on the quantum of usage of services and offers customized plans accordingly. This can help nudge customers to try out premium plans with more and better features. Or say, an online apparel retailer can choose to conduct style surveys to identify product preferences and accordingly suggest offers most suited for the target segments.

It is also possible to A/B test promotional offers for specific customer segments to determine the highest performing ones.

Conclusion

Even small scale ecommerce stores can generate tremendous amounts of order and click stream data which can be difficult to store and analyze. Unstructured and semi-structured data generated from social media further add to the complexity of ecommerce analytics. Also, for high converting intelligent offer delivery, it is essential to have a holistic strategy that based on thorough analysis of customer demographics, behavior patterns, product preferences, location data and much more.

From the selection of the right analytics tools and infrastructure to the design of highly effective statistical models for offer design, ComTec helps clients dramatically improve conversions in short periods of time. For inquiries, email us at rpa@comtecinfo.com.

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