To develop compelling personalized campaigns to help its clients win back lost customers, Goodbyehello needs to understand why each customer stopped engaging with those clients in the first place.
Goodbyehello uses a cloud-based cognitive analytics platform from IBM to help it find correlations in customer-related data and quickly diagnose the main causes of customer attrition.
300%increase in customer re-engagement for one major client
Automaticdashboards save hours of data formatting and visualization work
Flexiblecloud-based analytics model requires zero up-front investment
Business challenge story
Winning customers back
When Goodbyehello’s founders came together in 2015, they brought with them a wealth of experience in industries such as insurance, telecommunications and e-commerce – where having an effective customer retention strategy can save millions of dollars in sales and marketing costs.
Most large companies in these industries have already made significant investments in systems and processes to mitigate the risk of customer attrition. However, the founders of Goodbyehello realized that very few of them have developed any effective strategies or tactics for winning back customers who have already been “lost”.
Bart Willems, Co-founder and Head of Customers & Opportunities, comments: “Today’s sales and marketing processes are often very highly automated, relying on sophisticated email campaigns to address carefully targeted groups of customers in different segments. However, when a customer stops reading or responding to these emails, there’s often no plan for what to do next. And once a customer has been inactive for a year or more, most companies simply give up on them.”
Nick Rood, Co-founder and Head of Data Science & Strategy, adds: “In many cases, companies are afraid to contact their inactive customers – they are afraid that the customer might have left because they are angry or upset. In fact, we find that this is very rarely the case. By far the most likely causes of customer inactivity are a change in personal circumstances, or simply the fact that they haven’t needed your products or services recently.”
The Goodbyehello team realized that if it could find a way to analyze the root causes of individual customers’ inactivity, there was a huge opportunity to develop personalized campaigns that would help its clients win them back.
“We knew that the success of our business would depend on the quality of the analysis we could provide,” says René Vetter, Co-founder and Head of Campaigns & Technology. “We needed an analytics platform that could help us enrich and explore a wide variety of datasets, identify the patterns and correlations that reveal the root causes of customer attrition, and present the results to our clients in an intuitive and compelling way.”
Building a business around cognitive analytics
True to its nature as an agile startup, Goodbyehello was keen to avoid investing in expensive IT infrastructure. It wanted a cloud-based solution that its consultants and analysts could use collaboratively anytime, anywhere – whether they were working in the office or on-site with a client.
Nick Rood comments: “Equally important, we wanted a solution that would produce results we could share directly with our clients. We have years of experience working with traditional statistics packages that produce output that only statisticians can understand. We wanted something much more immediate – because the cleverest analysis in the world is no use if you can’t explain to your client what it means and why you are recommending a particular course of action.”
These requirements led Goodbyehello straight to IBM® Watson Analytics™ – a cloud-based cognitive analytics platform that combines powerful predictive models and algorithms with automatically generated dashboards that can instantly present findings in a wide range of intuitive graphical formats.
To identify the root causes of customer attrition, Goodbyehello leverages both the “Explore” and “Predict” functions of Watson Analytics.
Nick Rood says: “Explore instantly provides suggestions that highlight possibly important patterns in new datasets – for example, it might recommend looking at the relationship between purchases and seasons, or holiday bookings over multiple years. If you think a related topic might be interesting, you can click on it to adjust the variables, and re-analyze the data instantly. It provides a very good quick way to find some interesting areas for further analysis.
“Meanwhile, Predict shows us all of the relationships between variables in the data. There might be dozens at first, but you can easily narrow them down to the most significant ones. The ability to let the data speak for itself, rather than just checking whether it conforms to your initial assumptions, is very valuable. Often it brings up relationships that might not be immediately obvious: for example, we’ve seen that people are more satisfied with their holidays if they have a very clear memory of them.”
The solution’s visualization and dashboarding functions are also important to Goodbyehello, as René Vetter explains.
“Comparing the visualization capabilities of other packages to Watson Analytics is like night and day. In previous jobs, we used to spend hours exporting cross-tabulations from our statistics system, manipulating them in a spreadsheet to create some charts, and then pasting those charts into slides that we could present to clients.
“By contrast, Watson Analytics automatically suggests a set of visualizations that it thinks might be appropriate, and allows you to select and combine them into a dashboard within a few clicks. You can even share the results directly with a client within the platform, complete with interactive filters that they can use to slice and dice the data for themselves.”
In particular, Goodbyehello has found that word clouds are a powerful way to communicate its findings to clients.
“We often ask inactive customers open-ended survey questions about why they stopped engaging with a client, and word clouds are an excellent way to show the most important themes across a group of respondents,” says Nick Rood.
“For example, for one retailer, the decision to stop selling a particular range of products had an impact on customers who were specifically loyal to that brand. And for a tourism company, we found that the most common reason for attrition was a change in family circumstances – for example, their kids had grown up and no longer wanted to go on a big family holiday every summer.”
Driving significant increases in re-engagement
By understanding why individual customers lose interest in clients’ campaigns and product offers, Goodbyehello is in a strong position to design more personalized marketing activities that drive a much larger percentage of lost customers to re-engage.
Goodbyehello recommends a three-stage process, known as the “ping, play, purchase” cycle. The ping stage checks whether the connection still exists – for example, does the person’s email account still exist? Next, the play stage involves designing a creative, entertaining and personalized interaction that will help to reawaken their interest in the client.
Bart Willems gives an example: “Perhaps they have some unused points on their loyalty card, so we ask whether they would like to keep the points or donate them to charity. The key point is that this first contact should not be a commercial offer. We’re inspiring them to think about our client again, we’re not trying to sell to them immediately.”
If the customer reacts positively to these playful approaches, the final step is to send a carefully selected product offer, encouraging them to make a purchase.
“Combining Watson Analytics with our methodology has delivered a 300 percent increase in re-engagement for one of our biggest clients – a major Dutch retailer,” says Nick Rood. “We’re very confident that the 300 percent figure is robust – it is based on more than six months of data. When we have enough data to run a similar analysis on our other clients, we will do so – part of the value of our service is that we can always quantify the benefits it brings.”
The platform’s predictive modeling capabilities are also valuable, as René Vetter explains: “Although it is too late to prevent the customers we’re looking at from becoming inactive, we can create models that allow us to assess the similarities between active and inactive customers, and help our clients predict which of their currently active customers are at risk of becoming inactive.
“For example, one key finding is that people who have purchased a product or used a service more than once are very significantly less likely to churn. So we recommend that our clients should target turning new customers into repeat customers, as the best pre-emptive measure to avoid future attrition.”
He concludes: “Our business model could not exist without IBM Watson Analytics – it gives us the insight we need to design truly personalized campaigns that really speak to inactive customers, and give them the encouragement they need to re-engage. The powerful analysis capabilities and instant visualizations help us show our clients not only how to win back lost customers, but also how to prevent further attrition in the future.”
Founded in 2015, Goodbyehello specializes in helping its clients recapture lost customers through a combination of advanced analytics, creative design and innovative personalized marketing. Founded by Nick Rood, an experienced data scientist, Rene Vetter, a loyalty expert, and Bart Willems, an advertising & media expert, the company is already working with a number of prestigious clients in a wide range of industries, including retail, wholesale, consumer products and travel.
- Cognos Analytics
- M&E: Connected Customer
Take the next step
To learn more about IBM Watson Analytics, please visit: watsonanalytics.com
IBM Analytics offers one of the world's deepest and broadest analytics platform, domain and industry solutions that deliver new value to businesses, governments and individuals. For more information about how IBM Analytics helps to transform industries and professions with data, visit ibm.com/analytics. Follow us on Twitter at @IBMAnalytics, on our blog at ibmbigdatahub.com and join the conversation #IBMAnalytics.