Time to read: 6 minutes
Results
3X
Product repurchase rate
>60M
Digital orders per year
The big win
Tillster, a global leader providing digital solutions like mobile ordering and loyalty programs for restaurant brands including Burger King, Jolllibee and Godfather’s Pizza, had seen success in personalization of its couponing programs and marketing campaigns, but felt it could do more to deliver even better results. Tillster needed to empower its brands with reliable, trusted data that could activate customized solutions and better engage end users.
To keep up with the competitive digital restaurant industry, Tillster turned to Twilio Segment to collect and unify more granular data. Now, with a modern data infrastructure, Tillster empowers its clients to provide more personalized loyalty campaigns at scale, which has resulted in increased user adoption and lifetime value.
Tillster powers digital ordering platforms for the world's largest food chains including Burger King, Jolllibee and Godfather’s Pizza. Its services empower brands to engage their end users through delivery, couponing and loyalty solutions. Tillster helps its clients provide great digital experiences to motivate repeat transactions among their customer bases.
Whether a restaurant brand is new to the digital ordering space or it has a more mature online presence, Tillster provides solutions catered to each client’s needs, wherever they are within their digital transformation journey. With customers spanning the U.S., LATAM and EMEA markets, Tillster enables more than 60 million million digital orders per year, and continues to grow that number.
To continue to empower its clients, Tillster needed the ability to better target communications and promotions at scale. While some of its data consolidation was automated, it was not always flexible or simple to activate. Tillster needed the ability to standardized data without relying on its Data Analytics teams and greater flexibility to activate marketing campaigns. Additionally, it wanted to collect more granular data, to stay ahead of the competition with highly personalized targeting. Tillster turned to Twilio Segment to standardize and activate its data across some of its largest clients.
In doing so, Tillster was able to:
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Increase data efficiency while reducing dependencies on Data Analytics resources
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Personalize communications and loyalty campaigns, providing end-users with the right message, at the right time
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Improve its ability to target specific customers with enhanced data collection capabilities
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Free up its Data and Engineering teams to focus on more strategic projects
Turning manual data collection into a scalable solution
Tillster supports some of the largest restaurant chains across the globe, many of which rely on its services to target specific audience segments for personalized campaigns. Prior to Segment, Tillster’s data collection was manual and rigid resulting in a lack of agility and flexibility. To create a segmented audience, the Data team ran manual queries, looking for users that performed a certain action (say, purchased a cheeseburger) and provided a .csv file for the Marketing team to use in its campaign.
On a weekly basis, Marketing sent 2-4 variations of these data-pull requests to Data Analytics, equating to 30 hours a month, on average, of manual work. This process of maintaining disparate data, collecting and delivering to cross-functional teams was not an efficient use of its Data Analytics resources.
Tillster leaned into Segment’s Customer Data Platform (CDP) to collect and unify more granular data, using Segment Connections. With more than 400 out of the box integrations, Tillster collects data from desktop browsers and mobile apps and sends it to downstream tools like Braze, Redshift and BigQuery. It also utilized Twilio Engage (formerly known as Personas) to integrate data into downstream destinations and empower more personalized communications and promotions for its clients. Segment has enabled teams across the Tillster organization, from Data Analytics to Marketing, to consolidate highly relevant data in a matter of minutes and immediately activate its client’s campaigns with real-time audiences.
“Segment has helped our marketing team become more self-sufficient. They are able to run queries, choose an audience size and set parameters on their own, without the back and forth manual data-pulls from my team,” said Farzana Noorzay, VP of Analytics, Data Science and Data Products at Tillster.
Utilizing Twilio Engage to activate relevant communications and coupon promotions
Prior to Segment, Tillster’s loyalty campaigns and messaging were not as precise as they desired, which resulted in limited personalization abilities. This was largely due to the cumbersome nature of data collection in a pre-Segment world. By the time its Marketing team requested a data pull, got that list from Data Analytics and uploaded the data into a downstream tool for a campaign, it was out of date. With Segment, Tillster can collect and activate data in real time, delivering extremely relevant campaigns to end users.
“Segment has really helped increase our efficiency and speed to market, so we now make better use of our personalization, and our time,” Farzana says.
Tillster relies heavily on Twilio Engage to build real-time, intent-based audiences for campaigns, ranging from on-boarding series to personalized coupon assignments to browse-abandon campaigns. Previously, Tillster depended primarily on purchase behavior events alone to enable campaigns. Now, it collects more diverse behavioral data. Segment allows Tillster to collect additional events and traits, create computed traits and stitch together complete customer profiles to build precise audiences for targeted campaigns.
Tillster’s clients heavily use its “Personalized Couponing” product feature. By utilizing Persona’s computed traits, tracking events like ‘item_purchased’ (say, a pizza) and calculating ‘average_spend’, Tillster clients deliver coupons that are highly relevant to each end user.
For example, if a restaurant’s customer spends $15 on an average order, Tillster can assign them a personalized coupon based on their behavior, such as “Get free delivery when you spend $20 or more”. Alternatively, someone else with an average order of $20 could receive the same offer with a higher minimum spend requirement. These types of highly targeted coupons deliver great value to the consumer and restaurant alike. “We use Segment on a daily basis to create audiences to target and personalize the offers and communications that go out to our restaurant clients’ guests,” saidAdam Morse, Senior Director of Marketing. Highly relevant coupons not only garner increased transactions, but impact important Key Performance Indicators like Average Order Value.
Utilizing data responsibly, to provide better personalized customer experiences
To build powerful, personalized communications and coupons, Tillster needed to graduate from collecting only transactional data to gathering behavioral and intent-based data as well. With Segment, Tillster can understand restaurant guest usage, browsing behavior, product preferences and more; all with real time delivery of data to all its tools and systems. “Segment allows us to collect a lot more data, create audiences and iterate on the fly. We now collect data that we weren't even considering before, because it would have been too cumbersome to get our hands on,” said Adam.
Tillster uses Segment to power personalized marketing campaigns for one of the leading dessert brands in the U.S. It tracks events related to browsing, clicking and viewing cakes as well as purchase history, and creates campaigns to re-engage users, based on their preferences.
Adam says, “With Segment, we are able to use more personalization and intelligent targeting.” One example of providing a highly relevant customer experience, and building trust with its customers, is showing up for important, annual events. Tillster knows that customers buy cakes for big life events, such as birthdays. By collecting events in Segment like “cake_purchased”, item purchased, and time of purchase, Tillster has been able to reach customers ahead of their buying cycle with automated targeted campaigns, reminding them to buy again. As a result of this campaign, the cake repurchase rate has more-than-tripled.
Another great example of utilizing customer data to provide exceptional customer experiences is a campaign that a leading burger chain built with help from Tillster. Using Segment, Tillster tracked menu item affinity to power various campaigns to serve highly relevant coupons and associated communications that feature the food items each customer likes and wants.
Loyalty programs to incentivize mobile orders
Since the pandemic, more and more of Tillsters' client base has introduced mobile ordering. In response, one loyalty client expedited the launch of mobile ordering through Tillster. Using Segment, Tillster connected its own loyalty platform and messaging tools to create a cohesive multi-channel campaign.
As part of this campaign, Tillster sent messages via email and push notification, incentivizing users with bonus points to try mobile ordering. With this campaign, Tillster’s client saw a more-than 50% increase in mobile order adoption. In addition, the users that ordered during that campaign became repeat customers.
One of the objectives Tillster identified as they set out to implement Segment was improved targeting and personalization. With Segment, Tillster can activate its data for more advanced targeting that produces meaningful results for its restaurant clients.
What's next?
Looking ahead, Tillster plans to focus even more on personalized campaigns by further leveraging its AI-based Recommender, automated predictions and Twilio Engage. In doing so, Tillster believes it can provide more relevant and powerful customer experiences. By tapping into existing ordering habits, Tillster wants to support order preferences, rather than encouraging users to do different things each time, which results in poor customer experience. Additionally, Tillster’s Data Team wants to get more fine grain with the analysis after pre post analysis by utilizing Segment.
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