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Portrait of author Catherine Frame
Catherine Frame

Head of Commercial Product Marketing

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The future of MarTech

By Catherine Frame on September 7, 2022

For retailers, customers’ expectations are higher than ever before. Providing customers with the convenience they now demand is a top priority for brands to thrive.

The good news is that the proliferation of data means that modern retailers have all the ingredients they need to execute perfect personalization strategies, right at their fingertips.

But there’s only one way to do this well – and that’s with a robust and well-considered MarTech stack, powered by artificial intelligence (AI).

Why AI? Because AI is the most powerful way to understand and act on the myriad of signals provided by each individual customer. When used correctly, AI can identify patterns and trends hidden in historical data to improve the customer experience.

More importantly, it can use that insight to predict a customer’s needs or behavior and enable the retailer to tweak the experience for both customer and commercial benefit.

However, there are very few retailers who have the ability to view, manage and harness their data in this way. Most retail or consumer businesses we meet are struggling with data silos, or systems of record that don’t talk to each other. This results in personalization being executed via a single channel – if at all.

Introducing AI into the marketing tech stack will change that.

What’s the most common approach?

For many retailers, the current approach to assembling a martech stack is clunky and suboptimal. Quite often each piece of marketing technology has its own data set, usually linked to the channels that that piece of technology is aimed at.

While this is great for looking at individual channel performance, it risks creating silos in a business – especially from a data perspective.

When data is contained within a single system (siloed), it’s isolated from the rest of the business, and it’s only ever accessed by the end user of that system. If this data is then used to make decisions, the end users lack the full picture needed to make the right judgment.

For example, in the context of customer acquisition, it is vital that every data source is used to establish high value customers. For instance if returns data is not ingested into customer profiling, the result could be a very skewed view on which customers are most valuable to a business – if only transactional data is considered, we could deem customers with a large basket value and/or frequent purchase to be customers we want to attract more of.

However if all/a high number of those items purchased are returned, not only are those customers potentially costing the business money, they are not the customers you would want to attract more of. Looking at all customer data touch points ensures that customers acquired are highly profitable, active and engaged customers. 

For example, a retailer’s marketing team sees a surge in engagement with their latest product post on Instagram. So, they promote the product further, including it in boosted posts and on their social stories.

However, what the team doesn’t consider is the number of units left in stock for that item – as they have no visibility of this information. This item sold really quickly, and now only has fragmented sizing left – so, when the majority of customers follow the link to purchase, their size is not in stock, causing huge bounce rates. 

You cannot afford to underestimate the impact that such an experience can have on a retailer’s customers. In an age where consumers are increasingly expectant of personalization, with 83% happy to share their data for a more personalized experience, if products are recommended that are unavailable then this will inevitably disappoint the customer. The breakdown in both data and cross-functional working here has resulted in a large number of customers having a poor experience and, potentially, will make them unlikely to shop again. The time to change this is now.

What’s also often forgotten about is the impact data silos can have on internal teams. Data silos lead to culture silos – and most retailers want fully cross-functional teams, all working towards the same common goal. In order to do this, there must be a single source of truth to dictate success. If this isn’t established, then teams are working in vain to try and achieve a truly omnichannel approach.

The solution is a connected approach that unites data from across the entire customer journey. By analyzing every customer data touch point together, retailers gain a complete view of behavior, this can be leveraged to increase both customer experience and commercial performance. It’s an approach that will become commonplace, not least because it offers a significant competitive advantage.

To achieve it, marketers need three core things:

  1. Data unity: All data in one place, so every channel and system can use every single datapoint, both first and third party.
  2. Predictive insight: Identifying patterns and trends that can inform decision making against key strategic objectives.
  3. Feedback loop: Ensuring continuous learning, the performance of each action being measured and systems are iterated and improved.

Or, to put it another way…

Centralized data + artificial intelligence + execution platform

1. Centralized Data – Snowflake

A powerful data warehouse, Snowflake enables users to organize data in a structure that is most meaningful to their business. It centralizes data, creating a repository from which all MarTech solutions can run – both now and in the future.

The Snowflake Data Marketplace also provides access to third-party data, allowing users to leverage insight from the wider market as well as their businesses to enhance MarTech performance.

2. AI platform – Peak

On Peak, users can rapidly deploy multiple AI applications from a single platform, eliminating the need to leverage a complex web of individual solutions. The platform can be used by both technical and non-technical teams, giving decision makers access to a single, predictive view of their customers.

Filters and segments are fully customizable, so users can leverage the platform and its apps to deliver on the objectives that matter most to their business, for example, identifying when customers are in-market to purchase, and the specific items they’re likely to buy.

3. Execution platform – Braze

Braze is a comprehensive customer engagement platform that powers relevant and memorable experiences between consumers and the brands they love. Context underpins every Braze interaction, helping brands foster human connection with consumers through interactive conversations across channels that deliver value quickly and continuously.

The result?

A best-in-class functionality and user experience, for a fraction of the cost and complexity involved in self-building or leveraging multiple systems. Not to mention the elimination of silos across your business and increased customer loyalty. 

In this new age of changing customer expectations, the agility and personalization offered by a simplified MarTech stack will separate the winners from the losers.

Ready to take your MarTech stack to the next level?

Supercharge your decision making with AI. Book a call with Peak's expert team to learn more!

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