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4 predictions for the future of cross-channel automated optimization

March 23rd 2022

Mike Austin

By Mike Austin

CEO & Co-founder

4 predictions for the future of cross-channel automated optimization - featured image

Testing and optimization is a vital part of any healthy marketing machine. It’s how marketers can discover what’s working for their business and what’s not, and focus their attention accordingly.

Despite this, research from Phrasee shows that less than 1 in 4 marketers are doing any type of testing to understand what’s resonating with customers. And of those who are, the majority are only doing basic A/B testing.

An explanation for this could be that testing and optimization has traditionally required a significant time investment, which is something most marketers don’t have a lot of.

That’s why we believe automated optimization is the future. It gives time-poor marketers the opportunity to continuously test and improve their marketing at the touch of a button, so they can focus on setting strategies rather than monitoring test results.

What’s more, automated optimization technology that enables optimization at scale empowers marketers to produce big gains that impact the business, rather than implementing small tests that don’t make a meaningful difference just to tick the testing and optimization box.

Here are our predictions for the next 5 years of cross-channel automated optimization technology.

1) Automatic AI optimization of sequenced messages

Sequenced messages, such as triggered emails, are an effective tool to help marketers save time and remove some of the manual work from sending messages to their customers.

Over the next 5 years, we predict that this will become an even more effective marketing tool with automatic AI optimization of sequenced messages for individual consumers across multiple channels, including web, email, IM systems, SMS and web push. We predict this level of optimization will be possible for content, cadence, length of the sequence and channel mix, providing a new standard for personalized messages.

2) Automatic optimization using multiple touchpoints and sources of data

The customer experience can be personalized using many different sources of data, such as:

  • Behavioral – based on a shopper’s activity, for example what they’ve browsed and purchased in the past
  • Contextual – related to a shopper’s context, for example their location
  • Offline transactions – what they’ve purchased in-store
  • Loyalty card data – such as zero-party data related to members’ interests
  • Returns data – based on a shopper’s returns history (i.e. items they have returned)

We predict that automatic optimization using these multiple touchpoints and sources of data across online and offline will be possible over the next 5 years.

3) Automatic adjustment of in-store and online experience for identified consumers

The pandemic has led to a boost in online shopping, but with physical stores opening up again, consumers are turning towards a hybrid approach across both channels. This means connecting the dots between the online and in-store experience will become increasingly important, and we believe automated optimization will be a key part of that.

We predict that decisions will be made in one central personalization platform, with data being fed in from both online and offline sources, such as the ones highlighted in the previous prediction. Offline systems will be able to call up the API once the consumer is identified (which could be via loyalty card, payment card or POS capture, for example), have the decision made by the personalization platform and the store clerk can then take the appropriate action in the offline world. An example of this could be a shopper showing their loyalty card in-store and the clerk then being able to recommend products based on previous purchases that have taken place both online and offline.

This could help tailor multiple elements of the customer experience at touchpoints such as clienteling, loyalty schemes, call centers, voucher issuing and packing slips for orders (for example adding personalized product recommendations to in-package slips).

4) Automatic testing and optimization of machine-generated content variants and discount strategies

Automatically generated content is in its youth at the moment, with companies such as Phrasee and Persado leading the way with AI-generated language and brand language optimization. We predict that automatically generated content will continue to grow over the next 5 years and that we’ll see AI working as a partner with marketers in content creation and optimization, rather than replacing them. Using automation in this way will help free up time for marketers, who can then focus on strategy and more important projects.

Another aspect of this is automatic optimization of discount strategies. Discounting can be an effective strategy to boost sales, but it costs real money. With automated discount strategies, businesses can make sure they’re hitting the sweet spot of discounts (in terms of percentages and timing, for example), to ensure the tactic is working for them.

Final thoughts

We’re in the early stages of automated optimization and we’re excited to see what happens in this space over the next 5 years. Fresh Relevance will be part of this journey, as we continue to help online businesses automatically optimize their cross-channel customer experience.

Learn more about the capabilities of our Testing and Optimization module and book a demo to find out how Fresh Relevance could help you and your business with automated optimization.

Book a demo to get started with testing and optimization today

Mike Austin

By Mike Austin

CEO & Co-founder

Mike Austin is co-founder and CEO at Fresh Relevance. Recognizing the challenge of data aggregation in the ecommerce space, Mike launched Fresh Relevance in 2013 with co-founders Eddy Swindell and Pete Austin to solve this need and optimize the customer journey.