Lijo Thomas

Lijo Thomas
Director, Product Management
Infosys Equinox

In 2023 and beyond, the strongest digital competitive advantage for enterprise brands, manufacturers and retailers is customer data and the intelligent application of this data within the buying journey.

In other words, personalized journeys are optimized journeys – for both the business and the customer.

With 86% of consumers saying personalization impacts their buying behavior, the companies that survive and thrive will be those that effectively capture, harmonize, analyze and utilize customer data across selling and marketing channels, digital touchpoints and post-purchase programs. And this effort is increasingly reliant on AI and machine learning to scale, automate and continually improve strategy and execution.

But this is easier said than done.

Only 18% of organizations have fully implemented personalization

While commercial apps for personalized product recommendations are widely available as-a-service, personalization involves much more.

Beyond “you may also like,” personalized pricing, offers and customer service are positively correlated with strong business performance.

Surveying over 2,500 digital commerce leaders, the Digital Commerce Radar 2023 reports that only 39% of companies have fully implemented personalized customer service, and only 18% have completely implemented personalized offers and pricing. The adoption of these capabilities increases a company’s likelihood of being in the top 25% of companies by 13%.

What hinders full-fledged personalization?

1. Data collection and integrity

In today’s age of Internet privacy regulation (aka the “cookie-pocalypse”), valuable first party data is increasingly difficult to collect. Whatever portion of visitors reject your cookies, block cookies, browse incognito or switch devices through their journey will leave a gaping hole in your aggregate data.

“Bad” data also muddies the waters. Data that is outdated, incorrect or irrelevant hurts personalization – it's better to not target at all than to target the wrong things.

Add in the complexity of omnichannel retail touchpoints and siloed systems, and the challenge compounds. Siloed data makes it difficult to achieve a reliable 360-degree view of the customer and serve consistent, contextual experiences. For example, the website and digital retargeting can miss the fact a customer purchased and returned a given product in the offline channel.

Disconnected data also makes it far more difficult to properly attribute sales to digital marketing programs and strategies.

For brand/manufacturers and CPG companies, “owning” customer data is even more challenging, as loyal customers often purchase through multiple retailers and marketplaces. Customer profiles based on browsing and purchase history through the D2C channel may not factor that a visitor is a raving fan with a strong lifetime value, or a propensity to buy full price, frequently.

2. Complexity

Underneath personalization tools lie sophisticated algorithms and machine learning models. These models can be complex to build and fine-tune and require internal expertise in data science and artificial intelligence.

Off-the-shelf platforms exist but require large data sets and proper administration to get the most mojo from. They also typically focus just on the Web, and don’t sync across phygital channels. This impacts the quality of data and ROI.

The bright spot is recent advancements in open-source AI (such as Open AI’s GPT offering) is quickly democratizing this space. We may soon see more data management in the hands of everyday business users, given the right evangelism of what tools to employ and how to use prompts effectively.

3. Integration challenges

To serve personalization across channels and contexts requires sharing of data, and integration between the platforms and tools that need to communicate with each other to overcome silos. For example, the digital commerce platform needs to talk to loyalty services, promotion engines, CRM, billing systems, marketing automation and even POS systems.

While large enterprises often have more budget to invest in such heavy-lift integrations, they also are more likely to have widely different systems across business units, including inflexible legacy systems that are more costly and more time consuming to build connectors for – and these highly custom integrations can be more error prone after rollout.

4. Resource constraints

Effective personalization programs require significant resources, from data storage and computing power to the cost of skilled people. Personalization is not a project, it’s an ongoing program that requires budget allocation across teams.

5. Lack of cross-functional cooperation

For many large enterprises, it’s not just technical systems that are siloed. If leaders across sales channels and divisions need to share data but are not aligned in shared goals and compensation structures, there can be resistance against executing the technical and business roadmaps required to achieve omnichannel personalization.

Overcoming integration challenges with MACH-X

Even under ideal conditions where you have solid data collection, advanced AI-driven tools, skilled talent and interdepartmental buy-in, data needs to be shared across multiple systems and end-user experiences and orchestrated to accommodate your custom business logic and personalization rules.

Without this cross-system communication, you’ll have personalization experiences that only serve isolated channels and touchpoints, and don’t add value to the “big picture.”

MACH-X makes integration much easier

Microservices-based, API-first, Cloud-native, Headless and eXtensible (MACH-X) architecture leverage the flexibility and efficiency of API orchestration and enable faster and more cost effective integrations between systems.

Even when your data sources are legacy monoliths, APIs can effectively extract data and share it with your microservices orchestration layer, where your business logic and personalization rules live and are managed.

For example, an end-to-end enterprise personalization program typically involves the following applications:

  • Customer Data Platform (CDP): Captures customer behavior across channels (including digital, offline, social media and marketplace) and matches back to behavioral attributes.
  • Data Validation : An ETL (Extract, Transfer and Load) platform accelerates data cleaning by extracting data from various systems and reformatting it for use with your AI/ML models. An ETL can automate the removal of irrelevant and duplicated data and normalize data without manual process.
  • Customer Intelligence Platform (CIP): Transforms unconnected data from across the ecosystem into a connected data fabric, to distribute across consumer touchpoints and marketing channels at scale.
  • Digital Commerce Platform: A composable and modern MACH-X digital commerce engine enables individual microservices to extend to any consumer touchpoint or back-office system.
  • Customer Relationship Management (CRM): Stores customer profile and contact details, tracks customer interactions and keeps customer account info up to date.

Plus, POS, Martech engines, back-office systems, packaged personalization engines, loyalty applications, BI tools…whatever stores and holds data pertinent to the personalization use case.

Microservices architecture enables the connection and communication between these powerful systems via APIs, streamlined within an orchestration layer. This makes it much faster to integrate systems, model business logic and make changes over time, as business needs evolve.

Reach out to us at to discuss how Infosys Equinox can help you accelerate your personalization journey.

Are you looking to build personalized commerce journeys for your customers?

Contact us to discuss how we can help address your integration challenges to craft hyper-personalized commerce journeys using our MACH-X platform.

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