Relationship Marketing In Custora – New Software Release

“What can we do this month to nurture ‘almost VIP’ shoppers into ‘VIP’ customers?”

 

“What churn prevention tactics do we have in place for our highest value customers?”


“How are we converting our 1x buyer, fashion-focused customers to make a 2nd purchase?”

 

We all know marketing is about building long-lasting relationships with our customers. Yet with all the new marketing technology that has emerged over the past few years, we still find ourselves caught up in the busy short-term needs of manual list pulls and frenzied scrambles to get the daily email out the door. Marketing is busy, and marketing is hard — and there is usually little, if any, time for questions such as the ones above.

 

The new release of Custora is designed to make these types of conversations a reality. Over the past few years, we’ve been fortunate to work with some of the biggest names in retail, large and small, and young and old — and we’ve expanded the software to capture best practices across the board.

 

These include the “playbook” that most successful retail marketing teams follow for formulating and executing marketing strategy:

  • Opportunity sizing and prioritization: what opportunities emerge from the data — and where should the team focus its time and resources
  • Testing and iteration: what ideas and messages are the most effective for reaching different customer segments
  • Automation: how can the best ideas be automated to create a sustainable “retention system” over time?

 

How do teams actually use the new release in practice? Let’s imagine that BigStore USA, an ecommerce retailer, were to log in to the new retention dashboard. They’d see a breakdown of the most important customer-centric Key Performance Indicators (KPIs), tracking the strength of their relationships with customers throughout the lifecycle. (Click here to learn more about these KPIs.) And an opportunity sizing would show them the gains, that they could expect, benchmarked against results from similar retailers.

 

Exploring their data, they might discover that their most significant opportunity involves increasing the purchase rate of customers who are active and engaged with the brand. By navigating to the Retention Marketing Manager, they could then access the full array of triggers available to them to reward and deepen the loyalty of their most valuable customers. They would also discover the opportunity to launch “programs”: recurring campaigns focused on strategic segments of customers, like their VIPs and almost-VIPs.

 

BigStore USA would try a variety of ideas to surprise and delight their VIPs. One month they might try exclusive sneak-peek access to a new shoe collection; the next, they might try a thank-you note from the CEO. And for their “almost VIPs,” they might try rolling out the red carpet with free shipping upgrades and personal shopper consultations.

 

As they experimented with different approaches to loyalty cultivation, they would discover what ideas worked best for each segment — and the retention dashboard would track exactly how much their efforts were moving the needle on the relevant KPI. Over time, they would continue to iterate and automate their best-performing ideas to capture the full opportunity from this segment.

 

We’re excited to have the opportunity to share this new release — stay tuned to learn more about how other retailers are using Custora to power relationship marketing!

[Press Release] Custora Raises $6.5MM Series A to Integrate Predictive Customer Insights into the Marketing Cloud

New funds will accelerate Custora’s product development and growth; helping marketers improve customer acquisition and retention.

 

New York, NY – April 7, 2015 – Custora, a marketing software startup based in NYC, announced today the close of a $6.5MM Series A led by Foundation Capital with participation from Greycroft Partners and Valhalla Ventures. The new funding will enable Custora to scale out sales and marketing and more rapidly develop the company’s B2C marketing software platform. Custora, a Y Combinator company, raised its seed round in 2011 and has been growing based on customer revenue over the past few years.

Custora provides a predictive marketing platform built for e-commerce teams. Its software analyzes data to predict how customers will behave in the future – the things they’re likely to buy, how much they’ll spend, even how often they’ll shop. These customer-specific insights enable brands to advertise and communicate in more effective and meaningful ways.

“It’s a new type of software – part predictive analytics, part B2C marketing automation,” says Corey Pierson, Co-Founder and CEO of Custora. “The goal is for marketers to spend more time thinking about how to treat their customer segments at different points in the buying cycle, and less time caught up in data analysis and list management.”

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Member Lifetime Value

Up until now, our predictive modeling at Custora has focused on understanding the behavior of paying customers. We’ve traditionally analyzed customer purchase patterns over time – and helped clients answer questions like, which channels, ad networks, or affiliates should I be looking towards to attract more customers like my highest-value shoppers?

However, lots of companies don’t directly acquire new customers – they acquire new members and then convert them over time into paying customers. This business model, often called “free-to-paid,” has become an increasingly common fixture in the e-commerce landscape. The model is now the norm in the daily deal and flash sale industries, where companies tend to sign up lots of unpaid subscribers, hoping to convince them to shell out for goods or services at some later point.

For these companies, homing in on customer lifetime value is obviously still important. But free-to-paid firms tend to spend acquisition dollars on getting new members – who may or may not go on to become customers. The only way for them to maximize their return on acquisition is to be able to predict how much a member will be worth over time, even when her first purchase may be far down the road.

With this challenge in mind, we recently introduced a new feature in Custora called Member Lifetime Value (or MLV). Now our clients operating a free-to-paid business model can see the predicted value of a new member. And they can further break down expected member value on acquisition factors like channel or demographic variables like geography.

This is a big win for clients like LivingSocial. But we’re sharing this to highlight some of the interesting modeling questions that come with changing the frame of reference from customer to member. Predicting member CLV requires the joining together of two separate models – a conversion model, and then a customer behavior model conditional upon conversion. So what are some of the main hurdles?

1) Conversion is all about timing. Across free-to-paid businesses, a certain proportion of members (usually between 5 and 15%) generally make a purchase immediately upon signup. Retailers shouldn’t ignore these customers – they tend to be particularly valuable, and may justify special attention to keep them coming back. But what about the vast majority of members who don’t convert right away?

Consider two identical members who both sign up at time t=0. Once converting to a paying customer, each member will go on to make regular purchases every four months, with each purchase netting $50 in profit. If member A converts at the 8-month mark, his expected two-year profit (starting at time t=0) is $250. But if member B converts at the one-year mark, his expected two-year profit (still starting at time t=0) is only $200.

In other words, conversion isn’t just a binary, “yes/no” variable with a single probability estimate. In order to predict a member’s long-term value, we need to model the entire distribution of possible times when they’ll convert.

2) Members, like customers, are all different. Customers come in all shapes in sizes – some make small purchases once a week, others splurge on big-ticket items once in a while, and many are “one-and-done” shoppers unlikely to return for a second purchase. It’s precisely this diversity in customer behavior that allows us to effectively use segmentation and targeting tools to get more efficient with marketing dollars.

The same is true of members. Some types of members are likely to convert very soon after signup, whereas others may take much longer to convert – if they ever do. Take a look at the following graph of member conversion behavior by acquisition keyword campaign for an actual free-to-paid company:

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For this company, members acquired through Keyword 3 were much more “conversion-prone” than those acquired through the Keyword 2 – by the end of one year, almost twice as many of them converted into paying customers. A robust model of conversion needs to factor in this underlying heterogeneity of conversion propensities across member segments.

3) Factoring in covariates. How do we predict at the moment we acquire a specific member how likely he or she is to convert into a paying customer – and when? We use covariates: the secondary data that a member record is “tagged” with at the time of registration. Variables like what channel or referral site a member came from can provide important clues about her underlying, unobserved likelihood of conversion.

4) Putting the pieces together. The conversion model is an essential component of understanding member value – but it’s only half the story. The other half is what the members actually do once they convert into paying customers. For example, a company might discover that members who sign up through a certain affiliate tend to be quick to convert – but then go on to make infrequent, low-value purchases over time. Both of these pieces need to come together to inform a prediction of the long-term value of members sourced from that affiliate.

 

Ultimately, introducing MLV is a step towards helping marketers at free-to-paid firms make smarter acquisition decisions. Any questions or thoughts on how we tackle MLV? We’d love to hear from you!

 

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Introducing Custora 3.0

We’re excited to announce another major leap forward for Custora with version 3.0.

Lifecycle Marketing: right message, right person, right time

Lifecycle Email Marketing Decision Tree

Custora 3.0 helps retailers move beyond “Batch and Blast” e-mails to deliver targeted, relevant messages to each customer when it matters most.  Powered by Custora’s customer analytics, retailers can now automate personalized marketing. For example:

  • If Sally buys jeans and boots a few times a year, but she has been idle for 9 months, maybe something is wrong.  Send her a discount on the hottest boots and jeans of the season.
  • If Joe just bought his fifth pair of shoes, send him an email thanking him for his support and offer him a bonus item.
  • If Vesper bought cheese on a monthly basis for two years, but hasn’t made an order in a full 12 months, she’s most likely found somewhere else to shop.  Send her a significant discount and a highlight of what’s new.

Custora seamlessly integrates with existing email providers to make this type of marketing possible for any online store.  Retailers can run experiments to discover the most effective ways to reach out to all different types of shoppers at different stages within the customer lifecycle.

Introducing Brand and Category Affinity Analysis

Category Brand Affinity Cluster Analysis

In the past, retail shops truly got a feel of what their customers wanted.  If Bob was a customer who bought power tools and electronics, and Steve was a customer who bought furniture and art, the shopkeeper would have very different conversations with each customer.

Custora now employs sophisticated cluster analysis to enable online retailers to strike up similar, relevant conversations with their customers.  The clustering models uncover different customer archetypes – based on the collection of brands and products that customers buy over their entire lifetime.

As always, these insights are immediately available and actionable in lifecycle marketing campaigns.

Easier than Ever to Get Started

Retailers can now get started with Custora completely on their own.  Via a self-serve wizard, new retailers can link up with Custora in a variety of ways:

  • MySQL, Postgres, or MS SQL databases over a secure SSH or VPN connection.
  • Common ecommerce platforms like Shopify and Magento.
  • Or customers can push data to Custora via our API.

Custora can also pull in your Google Analytics for Ecommerce data to provide customer lifetime value analysis by acquisition channel.

On average, new customers are live in less than a day.

On the email marketing side, Custora now integrates with 9 email providers, including: Bronto, SendGrid, MailChimp, MailGun, Campaign Monitor, VerticalResponse, ExactTarget, StreamSend, and Emarsys.

New Tour and Training Mode

We’re always working to make the insights we offer as intuitive and easy to use as possible, but new customers or team members often ask us for a quick way to get an overview of the major features.

To help with this, we’ve built a new training and tour mode that walks users through the product and describes how to get the most out of Custora.

On Deck

As always, we’ve got a number of exciting new projects under way to push the state of the art in customer analytics and marketing software.  Stay tuned for our next set of updates, and let us know what new features you’d like to see.