Last updated: Turn on a dime: Business agility starts with customer data management

Turn on a dime: Business agility starts with customer data management


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With all the rapid, abrupt changes we’ve seen around the world and their impact on business, the pressure to be agile is off the charts. 

After a global pandemic drove up digital-only interactions 72%, humbling retailers that were slow to adopt adapt, the dramatic rise in inflation is forcing business to radically alter their pricing and delivery models. As input costs rise, global supply chains tighten, all while consumers cut back on their own spending due to rising prices.

The framework for managing through this is broadly called enterprise agility— an organization’s ability to quickly adapt to market changes. And the foundation for business agility is customer data management.

Business agility: Marketing shows the way

Amid rapid change, marketing always seems to lead, whether it’s communicating a price increase to customers, changing a product roll out based on regional market changes, or reacting to the sudden unavailability of a product due to a supply chain shortage.

Companies need to account for how their customers will react to change, and manage customer experience delivery as appropriate. But marketing is the only part of an organization that can move fast enough to react immediately to crisis.

Changing products and introducing new products is time consuming. Changing a sales organization with set targets can’t happen overnight. What can change fast? Marketing budgets, campaigns, website messaging, webinar content, and search keywords.

So, what does the agile marketing organization require? 

3 keys to agile business and better CX

Let’s use an example: A popular outdoor retailer runs a promotion for a new hiking shoe that is a “collab” with a trendy brand — and it goes viral. Suddenly, sneakerheads worldwide go crazy and start buying.

The retailer – who used to a steady and reliable seasonal buyer – is now flooded with orders, running out of stock, and adding a slew of new customers.

While most brands beg for such a moment, it’s the ultimate test of business agility, and a critical moment in time. You can win lots of new loyalists – or quickly become a flash in the pan.

Three elements of business agility needed for success are:

  1. Richer data
  2. Actionable intelligence
  3. Pervasive automation

First things first: Customer data management

To begin, you need the scaled ability to capture first-party data with consent. Every new sneakerhead coming to the website and mobile app must be encouraged to authenticate and engage.

This involves offering a give-to-get for new customers (free shipping or a discount) and, more importantly, a scaled mechanism to capture that user’s permission to message her in the future. The experience must be seamless as well as completely transparent.  

For returning customers, you must have the ability to unify everything you know about them on the surface – SKUs viewed, loyalty points and status – but also go a level deeper. What’s the true value of a customer? How many times do they return an item, and by what method? How often are they willing to pay full price?

This data is only accessible by connecting the backend (financial ledger and supply chain) data to the profile. With a limited supply of a new item, you want to sell out, but you also want to reward your most loyal and truly valuable customers.

This is only possible by connecting the backend of business data to the front-end of customer engagement. Call it ERP to CDP.

Business agility requires intelligence at scale 

If you’ve created a unified data model across enterprise systems, and have models that can predict true customer value and react to changes in behavior and market conditions, you still need to scale intelligence.

In other words, every customer cannot be evaluated individually, and every decision can’t live with a data science team.

How strong is your ability to implement a machine learning framework that updates customer segments based on new information? ML models need to be continually tuned to changes in engagement across channels and understand how pricing and availability for specific products alter behavior. They need to overlap segments to understand how different buyers of the same product react to campaigns and different outlets for marketing and advertising.

Lifetime value scores need to be calculated against ever-changing baselines. LTV can change based on product and customer mix over time, making yesterday’s big spenders tomorrow’s regular shoppers.

Going beyond marketing and advertising, what type of intelligence is required to create success in the call center, or an e-commerce site, or a sales call? Models are only as valuable as their ability to create value in the endpoint of a specific application.   

Automation: Putting data insights to work 

Back to our sneaker example, you’ll need to suppress low-value customers from the campaign for the popular shoes. When a certain color or size becomes unavailable, customers with those preferences must also be suppressed – or encouraged to pre-order.

Then encourage loyal customers to “buy now” or use their loyalty status to get placed in the front of the line. They need to be put into the call center queue first and, when they visit the website, have a one-click option for putting the right shoes into their shopping cart, with their shipping preferences already pre-filled.

When loyal customers come into a store and can’t find what they’re looking for, the point-of-sale system must give the retail associate a next-best-offer or action that has a high probability of success.

This is the new battleground in marketing – the ability to use intelligence at scale to render the right decision across both offline and online channels, in near-real time.

Customer profiles need to get progressively richer, starting with marketing and advertising interactions, including cross-CRM data from sales, service, and commerce touchpoints. But they also must go deeper to leverage insights that can only be derived from the backend: ERP data.

Intelligence must go beyond data science-provisioned models and scale with ML, such that the customer profiles can be frequently updated as lifetime value and propensity scores change based on real-time inputs.

To adapt to a fast-moving market, driving that intelligence into action must be as automated as possible.

This next phase of customer data management, which brings the backend of agile business process together with the front end of customer engagement, is not about the next-best action or offer. It’s about finding the next-best dollar.  

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