Using AI to Combat Amazon? Don’t Let Data be Your Achilles Heel
No matter if you do business offline, online or both, the reality is your brand is always competing with Amazon’s quest for world domination. The good news is the steady rise of AI and big data is providing marketers with enormous value, helping to make sense of massive data sets and find patterns that can optimize and automate programs across industries from insurance to retail and even CPG. Using AI can help you create a seamless, personalized, and responsive experience for your customers — whether they are shopping, saving for college, or considering a new car. Done right, AI can help you seamlessly reach your target audience at the right time, with the right message in the right place. But done wrong, there can be unintended consequences.
The fact is that the value of AI and automation is only as good as the underlying data sets that drive its algorithms. To explain: AI is a bit of a black box, where algorithms run behind the scenes before spitting out results. The complexity of AI means that there’s often little visibility into why and how data was interpreted. At best, flawed data will hamper the success of AI-powered marketing programs — sending your message to uninterested consumers, or not generating promised boost to sales or cost savings. At worst, the results can be more questionable, creating the potential for unintended bias in your marketing programs that can not only be embarrassing but also undermine the desired objectives and results.
So what can marketers do to ensure they have the right data underpinning their AI efforts? The answer lies in devoting the necessary cycles to sourcing and evaluating the data you’ll need to train your algorithms. This involves considering these four critical elements:
- Transparency — How is the data sourced? What are its attributes? Can you segment the data used for your analyses as needed?
- Precision — How is the data verified/qualified for inclusion in the data set? What metadata does the data set include?
- Size — How large is the data set? Is it sizeable enough to accurately represent the population and your customers?
- Timeliness — How recently was the data collected, and how often is it refreshed – to both add new data points and remove data that’s stale?
Data buying should be a team effort
… and not a hasty decision. In my experience, a thorough data evaluation can take a month or more. Your ideal data evaluation team should include not just business owners and product managers, but also data engineers and analysts. By spending the additional time and resources to ensure that you have the right data underpinning your AI efforts, you can better realize your automation vision, minimize the issues that do arise, and avoid reworking or scrapping a project altogether.
Pay closest attention to data quality
There’s a direct correlation between the overall quality of your data and the success of your business. There’s nothing worse than procuring data sets and starting to build training algorithms only to perpetuate an undetected issue inherent in the original data, and then have to fix it.
But I’ve seen how the quality of data can vary — which is why it’s important to have the perspective of multiple stakeholders during the evaluation process. Be sure the data you’re sourcing is enriched with proper metadata — that’s what makes it the most powerful.
Pay attention also to the precision of the data you’re sourcing, especially if its location data. Your provider or aggregator should take great pains to thoroughly analyze, corroborate and categorize their data. This mitigates against sourcing data that is inaccurate and/or even fraudulent which is all too common. The best situation is when you, as the buyer, get visibility into the origin and specific attributes of each individual signal in the data set.
These are the realities of big data: no data source is perfect, and despite your best efforts, issues with new technologies like machine learning and AI are bound to occur. By understanding how your underlying data is collected, cleaned, verified and assembled, you can derive maximum value from your AI efforts — optimizing the use of your internal resources while improving the customer experience — and minimize the issues that do happen to avoid costly or embarrassing mistakes along the way.
AI can be your greatest weapon to combat the power of Amazon — as long as it’s running on the best data possible.