MailChimp Adds Product Recommendations Blocks Digital Marketing News

 

Users of the email marketing service MailChimp can now add automatically generated product recommendation blocks into their B2C emails.

Data Scientist Neel Shivdasani announced the new feature in a MailChimp blog post, which detailed how recommendations would be generated, using ecommerce data linked to MailChimp from the client. The calculations behind the feature have reportedly been tested on data conerning millions of shoppers, provided by major MaiChimp clients. Shivdasani explained the process as follows:

  1. First, you connect your store to MailChimp.
  2. Then, we analyze your sales data and do the math for you.
  3. Finally, you drop a Product Recommendations block into your campaign and hit Send.

That’s it. We’ll generate personalized recommendations for products that your customers are likely to buy, saving you all sorts of time and effort and enabling you to do cool things like:

  • Include product recommendations at the bottom of your next regular campaign
  • Set up an Automation workflow to send product recommendations a couple of weeks after a new customer buys something
  • Send product recommendations to customers who bought something a while ago and have been inactive since

Personalised product recommendations were once the near-exclusive preserve of e-commerce’s biggest hitters. Now, thanks to MailChimp, they are at long last within reach for the average brand.

Action it!

We imagine most of the MailChimp marketers reading this article will be eager to try out personalised recommendations at the next opportunity. Just follow this link to get started.

MailChimp will automatically perform all the calculations required to generate your customers’ recommendations, so you needn’t worry on that account. It’s worth considering that the more sales information you can provide, the better informed the recommendations created are likely to be. We predict a steady increase in the performance of personalised recommendations over time, resulting from this factor.

Gauging the effectiveness of using personalised recommendations with certain demographic groups could also provide some fascinating insights. Try turning on recommendations blocks for certain audience segments, and find out where the highest rate of extra sales is achieved. The conclusions you take away from experiments like this can prove invaluable in informing your future decisions. For instance, if your users over the age of 50 are the likeliest segment to impulsively add an extra item to their cart, you might want to consider using other strategies aimed at attracting impulsive purchases in the future for that segment.

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