How looking at global consumer actions over time can give AI engines the data points necessary to improve accuracy and relevancy
Few technologies have disrupted business as quickly and completely as artificial intelligence. And after a wave of improvements to these engines, AI has become the essential tool for remaining competitive today.
The problem, though, is even the most robust AI engine is only as accurate as the data you put into it. So if you’re looking for radically innovative findings and you’re relying only on your primary data sources and customer models, you may be in for a big disappointment.
How can you empower your AI for better results?
To give AI an opportunity to uncover truly transformational insights, a business needs to consider adding in anonymized, passively observed behavioral data for additional perspective.
Why?
Well for one thing, if your data is missing some key contextual hooks, your AI engine might conflate meaningless or random user actions as true interest or intent to buy. Or if your data doesn’t represent a large enough time frame, your AI assistant may fail to recognize an evolving trend. And if your sample size is too small, an AI tool could confuse outliers as real insights.
But when you include an alternative data source, like clickstream data, you can start to solve these challenges. In the case of clickstream specifically, for example, you’re adding in the perspective of:
- A global consumer panel of millions, for clearer, more reliable insights
- The ability to move years back in time to see emerging trends more definitively
- A more complete map of anonymized consumer actions for additional clarity about their behavioral group intent
It’s not so much that this data is better than the data that you already have. It’s more that it can provide your AI with the added information the engine may need to enhance the breadth, scope and authority of the conclusions it reaches.
Where can it help your business?
Yes, alternative data sources can enhance the effectiveness of your AI-based research tools. But it’s probably time we got more specific. So here are just a few of the many practical applications of clickstream data for improving AI results for your business:
Enhancing marketing effectiveness
If your AI can only model an acquisition program based on who has bought in the past and who is showing direct interest in buying now, it might tell you that you’ve reached the extent of your market potential.
But once you add a clickstream data layer to your primary data, an AI may discover a growing interest in your product category over the past three years from a previously unimportant consumer sector or market region. This simple finding could completely transform your results and capture huge profits for your company.
Aligning product recommendations with consumer expectations
One of the key AI tools of online retail is the product recommendation engine (i.e.: “People also buy…”, “You might also be interested in…”). Whether you’re using it for competitive opportunities or upselling existing customers, relevance and accuracy are essential.
Using the clickstream data from a range of different retailers can expand an AI’s ability to identify new use cases and nascent needs present within your market. Adding this kind of detail could become exactly the catalyst you need to improve the quality of your recommendations and increase sales.
Improving automated customer interactions
AI chatbots have become a frontline mainstay in most businesses. And armed with both your customer database and what you know about your market, they can do a fairly good job about anticipating customer needs and prospect inquiries.
However when you arm your AI with the added perspective of a relevant clickstream source, it can help you understand the common pathways consumers take when interacting with chatbots. It also can help you identify where consumers get stuck or abandon the checkout process. This allows your chatbot to self-correct and become even more relevant and effective when interacting with your audience.
Remember, AI may be an essential component in the future of business, but it can only react to what it knows. So stop limiting your company’s ability to respond more quickly to business opportunities and start identifying the holes in your data picture today.
