What if you were able to predict the items your customers are likely to buy, how much they’ll spend, even how often they’ll shop?

Predicting a customer’s lifetime value can be extremely important to retail brands who want advertise in a more effective and meaningful way to acquire the right customers & improve customer retention.

For example, Joey just purchased his first pair of hiking boots. Using a predictive Customer Lifetime Value analysis, advertisers will know right away if Joey is likely to be repeat customer.

Having access to this information allows advertisers to optimize ad spend towards channels that attract high-CLV customers, like Joey.

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We spoke with Brett Robbins, Head of Business Development at Custora to discuss how their advanced customer analytics platform built for retailers analyzes data to predict how your customers will behave in the future.

Q. Why is it so critical for retail brands to have a pulse on customer behavior?

A. Retail is more challenging than ever. Exponential competition (and Amazon) drive acquisition costs up — while customer profitability continues to decline. It’s a double whammy.

The days of simply acquiring new customers to double or triple the size of the business are gone.

For most retailers today the only way to grow profitably is to drive more revenue from their existing customer base and to deeply understand their high value customers so they can do a better job at attracting more of them to the brand.

There is lots of hype around “in the moment” personalization, using black box technology to deliver the right message at the right time to each customer.

But sophisticated marketers realize that the key to driving deep customer engagement is matching customer journeys with insights about who the customer is and what they intend to do.

This level of engagement is not delivered through the website and product recommendation engines available today.

It takes advanced analytics and machine learning to understand and predict preferences and behaviors. 

And a system that makes it easy to activate those insights in ways that help brands acquire more high value customers, accelerate loyalty within the customer base, and intervene when customers begin to veer off their expected purchase journey.

Q. How does an advanced customer analytics platform help retailers?

A. Custora is the B2C CRM system of the future. In 2010, our founders were studying marketing analytics and statistics at Wharton where they came up with the idea of Custora.

They were accepted into Y-Combinator, a prestigious start-up incubator, and officially launched the company in 2011.

Since then, we’ve been on a mission to help retail brands realize that growing customer lifetime value is the key to sustainable growth.

Today, hundreds of retailers use Custora’s CRM apps to help activate customer data to drive revenue and profit growth.

The apps are the first solutions that combine predictive customer insights, action, and measurement to help marketers achieve specific business goals such as converting one-time buyers into repeat buyers, reducing customer churn, and acquiring high value customers.

The apps use advanced analytics and machine learning to give marketers a deeper understanding of their customers, and the ability to prioritize and systematize action based on those insights.

Q. What metric(s) do you encourage retail brands to focus on & why?

A. Customer Lifetime Value. The one metric to rule them all (kind of). CLV is the most honest way to drive the business.

Most of the metrics retailers track today are a distraction – click throughs, engagement scores, unsubscribe rate, items carted, and so on. None of these capture the bottom line impact to the business – orders and profit from those orders.

While we agree it’s important to look at channels in isolation and use channel-specific metrics like the ones above to monitor performance, retailers need to transition to thinking about the customer holistically.

The customer doesn’t say, “let me go click on that email I saw on my mobile device while I am in the store to ensure the retailer can attribute my purchase.”

Focusing on customer-centric metrics with an eye toward long-term profitable growth will help the retailer make the best decisions.

In addition to CLV, we also recommend tracking and managing a few other customer-centric KPIs for each stage of the lifecycle:

— The Early Repeat Rate, the percentage of customers who have made a second purchase by a fixed point in time, is a powerful way to look at first time buyers and systematically increase conversion and build loyalty among those new to the brand.

— Reactivation/retention is equally important. We often think about improvements in this KPI as “plugging the leaky bucket.”

Q. What are some common pitfalls associated with predictive analytics & customer behavior?

A. Retailers today are busier than ever, and what they really want is a silver bullet.

Unfortunately, it doesn’t exist.

Overcoming the challenges facing retailers (promo expectations, email fatigue, etc.) requires a systematic approach to operationalizing segmentation across customer value tiers and the lifecycle.

We believe that the first step is shifting the mindset to the right metrics.

Then, you can operationalize programs in a crawl, walk, run approach – systematically measuring and improving segmentation starting with the customer buckets expected to drive the most growth.

Q. Can you provide an example where predictive analytics impacted a retail brand’s business growth?

A. Crocs uses predictive analytics to identify price insensitive customers, reduce the use of promotions, and increase bottom line margin. They have always had a data-driven, customer-centric approach to marketing.



In 2014, the Crocs marketing team was given a mandate to transform Crocs’ online business by becoming less reliant on promotions and discounts. The team was excited by the opportunity to improve Crocs’ profitability.

At the same time, the marketing team knew that plenty of other companies that reduced or eliminated promotions faced customer dissatisfaction, increased churn, and shrinking revenue.

Crocs partnered with Custora to implement a gradual, data-driven promotion strategy.

The team began by testing and optimizing promotions aimed at customers who were predicted to churn, and expanded the program to coordinate a “no discount” experience across site, email and display for customers with the lowest price sensitivity.

In addition to predicting behavior, Custora facilitated testing by integrating across channels and coordinating omnichannel measurement.

The results were huge.

Crocs is on target to significantly improve company wide margin while doubling revenue per shopper in test groups (compared to control). And this is all while reducing the use of promotions!

Q. Any predictions for 2017?

A. Most organizations do not purchase all of their marketing technology from one vendor. They use an ESP from one software company, a DSP from another, and an ecommerce platform from a third.

They may have a fully functioning CRM system in-house, outsource CRM via a third-party data service, or a customized data warehouse for their customer information.

The apps in the CRM Suite sit between the data and the executions systems. They utilize customer data from any source, and integrate with marketing execution systems to power customer-centric campaigns across channels.

We expect that in 2017 this trend will only accelerate.

Monolithic solutions from a single vendor will be a rarity. The CRM platforms used by B2C companies of the future will let marketers select that apps that are best suited for their business needs.

And these CRM platforms will be completely flexible and work in tandem with a wide range of data sources and marketing execution tools.

In 2017 Custora will be launching a suite of CRM apps purpose built to drive fast, sustainable growth by systematically increasing customer lifetime value. The Custora CRM suite is specifically designed for marketers and CRM teams responsible for leveraging customer data to drive growth.

Each app in the CRM Suite is made for a specific core function where CRM and Marketing teams collaborate. The apps are intuitive and easy to use, and integrate with existing marketing execution tools to power always-on customer growth initiatives.

The apps in the CRM suite are built on Custora’s Customer Data Cloud.

The Customer Data Cloud accepts data from any source and creates a unified view of the customer The Customer Data Cloud also powers the data science in each app with an ever expanding lineup of predictive intelligence models and and machine learning capabilities.

The Customer Data Cloud can also integrate with existing data science models, incorporating proprietary insights into the CRM Suite.

The Custora CRM suite will include the following apps:

1) Audience Activation
Any segment, every channel.

Gives marketers the ability to explore audience segments, create and publish target lists to a wide range of marketing tools, and measure the impact on customers across channels.

The Audience Activation app is simply the most powerful and easy to use segment creation tool available today. Better target your daily emails, identify audiences for merchandising initiatives, and gain a deeper understanding of your most important customers.

2) CLV Acquisition
Find better customers.

Marketers use the CLV Acquisition app to better understand their high CLV customers and use those insights to target future potential high-value customers.

They can also evaluate the performance of their acquisition based on the incremental CLV added to the business, answering questions like – “How should we shift our acquisition channel allocation strategy to acquire more valuable customers and fewer one and dones?”

3) Lifecycle Engagement
Optimize key leverage points in the lifetime journey

Marketers use the Lifecycle Engagement app to accelerate loyalty and intervene when customers begin to veer off their purchase cycle. Custora’s advanced algorithms continuously analyze customer data to identify opportunities for growth within each customer lifecycle stage.

The platform integrates with existing marketing tools to power always-on customer lifecycle programs, and measures the impact on the most important metrics that affect CLV. It matches where customers are in their lifecycle with a deep understanding of their preferences and

4) Customer Insight & Strategy
Surface hidden opportunities for growth

Shines a light on blind spots in the customer file and surfaces hidden opportunities for growth within the customer base. The Insight & Strategy app enables executives to analyze the drivers of customer growth, monitor progress against goals, and determine specific actions to be taken to optimize performance.

To learn more about predictive Customer Lifetime Value, email [email protected]

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