How Data Science Gives Small and Midsize Brands a Competitive Edge Against Big Brands

    Operators improve sales and targeting with access to better data.
    Sponsored Content | January 31, 2022
    How Data Science Gives Small and Midsize Brands a Competitive Edge Against Big Brands
    Civis

    Civis Analytics answers quick-service restaurants’ most fundamental questions: Who is your customer, what does that customer want, and how can you consistently meet or exceed their expectations? 

    The data science consulting and technology firm’s cloud-based Civis Platform unifies and cleans both first-party customer data (information culled from sources ranging from POS and inventory control to mobile apps and in-store kiosks) and third-party data sourced from trusted partners (such as demographics or geolocation info) to deliver clear, actionable insights enabling quick-service restaurants to more successfully compete within a fast-moving industry. These quick-service restaurants gain newfound confidence and clarity on addressing customer behavior trends—for example, delivering personalized menu offerings and loyalty rewards in lockstep with customer wants and needs—as well as uncovering new demand, creating more impactful marketing campaigns, managing store staffing requirements, and more.

    Crystal Son, the Chicago-based company’s head of data science communities, says Civis’s Platform is particularly valuable for small and midsized quick-service restaurants seeking to leverage data to cost-effectively compete with multinational conglomerates armed with large research and development budgets and other similarly outsized resources. “Smaller and midsize quick-service restaurants don’t have these luxuries, and so they really have to be disciplined about asking questions and solidifying what they want to get out of data science from the beginning,” Son says. 

    Platform’s data centralization and identity resolution (IDR) capabilities are vital to maximizing the value of quick-service resources, Son says. When data is centralized—collected, consolidated, and cleaned in a unified platform—restaurants can easily import, manage, transform, analyze, and report on all their customer information, allowing both data scientists and non-technical stakeholders to quickly access findings, make more confident strategic decisions, and more efficiently collaborate across teams and departments. 

    Using IDR, which connects data (phone numbers, email addresses, and more) from multiple sources, Platform finds matches among the records these sources contain and identifies duplicates to determine which sets of records correspond to the same individuals. Quick-service brands gain a unique, stable identifier for loyalty program members and other important customers, as well as a deeper, more comprehensive understanding of these customers’ previous interactions with the brand to enable more relevant, personalized engagements moving forward.  

    “There’s plenty of data, but it exists in 20 different databases. Some of it describes customers, and some of it describes transactions, such as in-store purchases or app behaviors. Some of it is updated in real time, and other data is updated every two weeks,” Son says. “It’s not until it’s all brought together and housed in a central location that you can actually make meaningful connections between those datasets and start identifying customers as actual people, which is the first step to understanding what they want.” 

    Platform also benefits quick-service restaurants lacking the data science prowess and experience of larger brands. “They’re reading a lot of industry articles about how data could be useful, but they may have trouble connecting the dots, like how to procure the third-party data they don’t have that would yield the most value to them and how to assess which third-party data vendors are legitimate sources,” Son says. “Our data scientists can be particularly helpful as consultants to newer or smaller quick-service restaurant brands. They may want to work in a proof-of-concept fashion, where they have a very short engagement at the beginning to learn.”

    Smaller quick-service restaurants can lean heavily on Civis’s service side to gradually transition to a more self-sufficient model, running analytics to optimize production and automation as part of everyday business operations, Son says. “Clients are always very optimistic when we tell them we do heavy consulting as well, because there are a lot of companies out there who license the software, and that’s it—and that doesn’t lend itself well to true adoption and engagement.” 

    Platform additionally offers quick-service restaurants a mechanism for hosting reports in a centralized spot that makes data insights available to authorized stakeholders across all levels of the organization, regardless of their physical location. 

    “Once you have your data in one place and you’ve done your analysis, you have to have a way of distributing those insights to important members of your organization that isn’t prone to bottlenecks,” Son says. “We can bring a lot of efficiency to their process and democratize those insights, so more people have the ability to see and track against those metrics. It helps with institutional alignment as well: If we’re all looking at the same goals, then we’re all able to work together to achieve those goals.” 

    It’s important for quick-service restaurants to establish some of these goals from the outset and come to the table with a preformed hypothesis about what they want their data to do, Son notes. This is particularly true in the case of smaller quick-service restaurants that might be early on in their analytics journey. “It doesn’t have to be completely set in stone. That’s how real-world data and analytics evolve. But you do have to come in and say ‘These are the levers that I have available for me to pull. I just need the insights to tell me which one of the levers I should be pulling.’” 

    Once their data is consolidated and accessible, and their objectives established, quick-service brands can begin putting their insights to use—for example, segmenting audiences into bite-sized chunks by factors like age, marital status, and household income, or identifying a specific customer within one of those segments and determining how to address their particular pain point or need. A quick-service restaurant can start by testing out mobile offers personalized to specific subsets of customers: “Then you know that the gains in your sales were truly from this offer versus any other extraneous variable,” Son says. “We run a lot of experiments to give quantifiable evidence that things are or are not working.”

    The data that results from these offers and experiments can guide where quick-service brands should focus next: which geographic regions are most promising for expansion, for example, or whether to invest in deepening relationships with customers who have proven their loyalty or trying to capture new business. 

    As mobile ordering, online promotions, social media advertising, and other digital trends continue to transform the quick-service segment, customer data—and the software to analyze it—increasingly looks to be the golden ticket to improving sales figures and marketing effectiveness across all quick-service restaurants. But smaller, leaner quick-service brands have a distinct edge: Larger restaurant chains subject to past acquisitions and mergers can have lots of “data architecture baggage,” Son says. 

    “Smaller quick-service restaurants have the advantage of much less of that infrastructure and more of an opportunity to build it right from the get-go,” she says. “Maybe they can’t think 15 years ahead, but just a couple steps ahead to decide what kind of data-driven, innovative company they want to be. That’s going to influence some of the decisions they make now about where to keep data, who it is accessed by, and what it is used for.”

    To learn more about how data can level the playing field for your brand, visit the Civis Analytics website.

    By Peggy Carouthers