Just like oil powers nearly all mechanization in the developed world, data powers nearly all marketing strategies and platforms. Without data, the digital economy would grind to a halt. For this reason, meeting the challenges of data and analytics is a necessity in today’s market. The brands that don’t meet these challenges fall behind or go out of business.
 
In preparation for an informative webinar on this subject, Loyalty360 spoke with Sarah Galloway, Director of Strategy at Aimia. During the webinar, responses collected from leading brands in the customer loyalty space will be shared. All attendees will receive a copy of the report following the webinar, as well as a recording of the webinar that will be available for a limited time.
 
What is the biggest trend or change you have seen regarding data and analytics over the last 18 months?
 
We’re seeing a significant investment in predictive models and tools that leverage machine learning and AI. As brands strive to demonstrate their customer-obsession through experiences that build emotional bonds, they’re turning to these tools to help them scale. Rather than building these tools from the ground up, which can be costly and take significant time, businesses are partnering with field experts that have tested and proven tools (e.g., offer engines, market basket analysis, predictive churn modeling, and look-alike modeling) to help execute campaigns efficiently.
 
What is important when developing your own set of new or refined data-centric KPIs?
 
The most important things to consider when developing your KPIs is aligning your team on what your data enables today, what you need your data to accomplish in the future, and why you need your data to accomplish this. This goes beyond simple loyalty program metrics. Loyalty data is key in identifying opportunities across all business units, whether it’s marketing, operations or buying. Where do you want your brand to be in a year or three years, and what is the measure of that success? With KPIs established, continually monitor your progress and refine your program from there.
 
With so much available data, how do you focus on the right information that can have the biggest impact?
 
It’s true that some marketers may be feeling acute data overwhelm in this age of boundless data. As a starting place, always go back to your KPIs. From there focus on the short and long-term approach.  Review what you can work with at this point in time, whether that’s transactional data paired with profile preferences or engagement data from campaigns. Use the data that will be most impactful in achieving your KPIs. In the short-term, make sure you don’t disregard other data entirely; keep your long-term KPI goals in mind. Each piece of information that is captured helps to round out a holistic view of your customer, what Aimia calls the GoldenProfileTM. With this data collection you can focus on your long-term approach. Analytics and machine learning tools allow marketers to not only identify major behavior trends, but also provides insight into outliers within these segments and the offers likely to appeal to them. Marketers can work smarter without data fatigue. This approach should help round out a focused data approach for your short and long-term goals.
 
What lessons can be learned from the large data breaches of the past in terms of securing one’s data set and in helping to keep a brand’s commitment to consumers?
 
Transparency and proactive auditing are key, whether a brand is using internal tools or partnering with a vendor. Businesses should leverage their team or consultants—IT, legal, and operations—to make sure that they and their partners are proactively adhering to best practices in terms of storing, securing, and backing up data. In addition, customers should have a clear understanding of what a business is using their data for, and how it’s being handled.
 
Where are most brands in their understanding, acceptance, and utilization of machine learning for data management?
 
Most brands are just starting to scrape the surface of machine learning’s potential. There are certainly some leaders—the Amazons and Nikes of the world—that are making the maximum use of their data. But it seems many brands are just dipping their toes in the AI water and, with that, are determining what their team should look like to transform data into action. As budgets get cut and the talent pool for analysts remains highly competitive, brands are looking to use scalable tools that have been tested and proven to upsell, cross-sell, identify customer lifetime value, and predict churn. Loyalty becomes a data mechanism to deliver this value to both the business and the consumer.  
 

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