The Hidden Cost of Legacy Systems:
Why They Fall Short on Customer Movement

The modern marketing tech stack — CDP, ESP, Marketing Cloud, or SMS provider — is packed with data solutions. As a result, retailers are equipped with mountains of data to inform their marketing strategy, including everything from customer acquisition and retention to campaign performance and product recommendations.

The majority of this data is not only siloed, but backward-looking.

It tells you what customers did in the past, but provides little insight into where they’re headed in the future. Understanding — and predicting — future intent is key to moving shoppers through the lifecycle from first purchase to second purchase and so on to drive profitable growth.

What’s missing: Streaming shopper identification, event data (behavior and product catalog), and prospect data. This is essential for determining intent and moving customers through the lifecycle, increasing conversion and retention, and growing the value of your customer file. To make matters worse, even if retailers have this data, it’s not easily actionable to deliver campaigns at the speed that shoppers are moving. 

Without predictive data at their fingertips, marketers are flying blind.

Channel and purchase data fuel the common list-based approach to marketing. Clicks and conversions are retroactive, static, and isolated. They tell you what happened on a single channel, but don’t connect the dots across all interactions a customer has with your brand.

Without a holistic and real-time view, it’s difficult to understand the “why” behind customer behavior and drive movement across channels effectively. Instead, retailers need a signal-based approach, which starts with real-time shopper identification. By the time a shopper clicks on an email or visits your website, your systems may not immediately recognize who they are or what stage they’re at in the lifecycle.

When you can identify a shopper, their preferences, and their lifecycle stage, you can act quickly and engage them with relevant messaging, proactively guiding them to their next interaction. That is the essence of customer movement, a marketing philosophy and framework, defined by customers rather than channels.

By combining retroactive channel and purchase data with streaming or real-time identity, behavior, and product data, customer movement technology enables retailers proactively move customers through the lifecycle.

The world’s largest PC company, Lenovo, used customer movement to increase repeat purchases by 6.5% and retention rates by 3.5%. Read their case study here.

Incomplete and inaccessible data limits your predictive capabilities and, by extension, your ability to respond to and stay ahead of shopper and customer needs at every phase of the lifecycle. To illustrate the impact, let’s look at hidden costs that miss revenue and waste resources, adding to the total cost of ownership for a retailer’s marketing tech stack. 

You miss valuable revenue opportunities

Customers expect their needs to be met fast. And if you’re unable to identify and fill their needs, they’ll simply go somewhere else — often for good. 

Without real-time shopper identification, you miss retargeting opportunities on digital channels. Without streaming event data on customer behavior, preferences, and product changes, you miss out on critical demand signals. Without the view into prospect data (non-buyers, lapsed, one-time buyers), you miss out on reaching the majority of your audience.

This lack of forward-looking insight creates unrealized revenue opportunities, including:

• Challenges converting shoppers to first-time buyers: When non-buyers repeatedly engage with your brand but aren’t met with timely, relevant experiences, you miss the chance to convert them into loyal customers. According to Bluecore’s 2024 Benchmark Report, the average retailer only identifies 21.1% of shoppers who visit their site, leaving the majority of visitors unaccounted. As a result, they’re unable to be retargeted.
• Missed upsell or cross-sell moments: Without real-time insights into changing preferences or behaviors, you lose out on opportunities to introduce additional products and categories that enhance the customer’s experience and increase their lifetime value. When a one-time buyer makes a second purchase, their CLV goes up by 102%. And when they make their third purchase, CLV goes up by another 50%. 
• Failure to re-engage lapsed buyers: Without identifying when a past customer returns to browse or shows interest in new products, it becomes nearly impossible to win them back. This is a huge miss as lapsed buyers represent a significant portion of the customer file. Once they’re reactivated, they spend 12.7% more, on average, than new customers. 

Your people waste valuable resources

When your teams work with backward-looking legacy systems, they’re forced to spend significant time piecing together data and insights to launch campaigns. Instead of focusing on strategy, marketers are dragged down by manual processes, working across multiple teams, pulling lists, and trying to make sense of fragmented customer journeys. 

This inefficiency often leads to slow, inconsistent messaging across channels and weakens customer engagement and loyalty, making it harder to build long-term relationships. Revenue suffers as campaigns fail to anticipate where target customers are headed.

And worst of all, this hurts purchase frequency, which results retailers sitting on a customer file full of one-and-done buyers that are eating into their growth. Since 74% of customers disappear after their first purchase, no retailer needs more of those.

Lost revenue isn’t the only bottom-line impact. Costs get poured into agencies or services that piece together data to launch and optimize campaigns. Working with a legacy system, one enterprise fashion retailer ended up spending an additional 25% on consulting and service fees.

Here’s the good news: You can avoid the hidden costs of legacy systems by layering on flexible technology that’s focused on customer movement. 

Customer movement technology is designed to proactively guide customers through their lifecycle, rather than simply reacting to past behaviors in channel silos. By integrating real-time data and predictive AI into intuitive campaign workflows, it enables brands to anticipate and respond to customer needs, creating personalized experiences that evolve with each shopper and ultimately increase the value of your customer base for long-term profitability. 

Retailers who focus on legacy, channel-led growth have a three-year customer retention rate of 22%. Meanwhile, their competitors who adopt customer movement see their retention rate soar to 59%.

The key capabilities to forward-looking customer movement technology are: 

• Transparent identification: Instantly recognize your shoppers and all their interactions, whether that’s browsing on your site or opening an email, and even through different browsers and cookie-clearing. Identification allows for the collection of behaviors and preferences that drive personalized, real-time interactions at every touchpoint.
• Real-time connection to the product catalog: Seamlessly integrate your product catalog into the customer experience, leveraging SKU-level data such as inventory levels, size, price, color, and category. Combined with identification, behavior, engagement data, this rich information feeds into predictive retail models, enabling you to match customer preferences and behaviors with the most relevant products in real time.  
• Signal-based messaging: Dynamic campaign workflows with point-and-click predictive AI models allow you to launch and optimize a broad set of triggers and signal-based messages in minutes. From abandonment and price drop triggers to fully dynamic full-list promotional emails, your marketing efforts align with your customers’ current needs at the moment they need it.

Workwear brand, Carhartt, is leveraging expansive triggers and automations to drive their 2024 goal of 5% YoY repeat buyer growth. Read takeaways from our recent conversation with Carharrt’s Director of CRM here. 

Looking past the past purchases

Purchase data — such as the product purchased, the price, the location of the sale, and the payment method used — is valuable for understanding past customer behavior. But it’s only part of the picture.

Relying solely on purchase data limits your ability to predict what customers will do next and to engage them meaningfully beyond the transaction.

Critically, retailers often miss out on key demand signals because they lack streaming event data that captures customer behavior, product preferences, and inventory changes as they happen. Without this, retailers are left guessing what products are in demand and when to engage the customer. To make matters worse, purchase data is often not linked with other behaviors like browsing history, wishlists, or abandoned carts. 

For example, imagine a customer who frequently buys workout gear: leggings, sports bras, and tops. Recently, they’ve started browsing the brand’s yoga mat collection and added one to their wishlist, but they haven’t made a purchase yet. If the retailer is only looking at static purchase data, they’ll see this customer as someone who typically buys apparel and may miss the opportunity to engage them with relevant messaging about a new category, yoga gear.

Customer movement technology integrates purchase data with key customer behaviors, such as current browsing history and cart activity, to generate predictive insights and personalized recommendations.

These insights help you consolidate data across all touchpoints, enabling consistent and personalized experiences whether the customer is shopping online or in-store. Because robust customer movement technology also monitors real-time changes in inventory and product details (new arrivals, out-of-stock items, price changes), recommendations are not only personalized, but timely.

Home goods retailer, Lulu and Georgia, tapped into customers’ category and discount affinities to increase repeat buyers by 299% and first-time buyers by 133%. Read their case study here. 

Reaching your full audience

Retailers often have deep insights into their regular purchasers, loyal customers who buy frequently and drive a significant portion of revenue. The problem is that most of the data focuses on known customers, leaving out critical segments of prospects: non-buyers, lapsed buyers, and one-time buyers. 

This narrow view prevents retailers from understanding and engaging with up to 70% of their potential customer base.

When retailers have limited data on non-buyers and one-time purchasers — such as what products they’re viewing, which channels they’re using, or what they’re abandoning in their cart — it’s difficult to turn them into loyal, repeat customers. Worse, without tracking data on lapsed customer behavior, retailers lose the opportunity to re-engage them and bring them back into the brand ecosystem.

For example, let’s say a lapsed buyer previously purchased a new sofa and hasn’t returned since. Without streaming data to track their behavior after that purchase, the retailer misses the chance to recognize when that customer starts looking for matching accessories, like rugs or coffee tables. As a result, they can’t send messages with product highlights, reviews, and limited-time offers, nudging them toward their next purchase.

Customer movement technology integrates prospect data with purchaser data. This allows for hyper-targeted segments, such as cart abandoners or price-sensitive browsers. More importantly, it allows you to deliver proactive insights and personalized strategies for converting prospects into loyal customers.