A penny here or there means nothing to most folks, but if you had a penny for everyone on this 7-billion-person planet? (That’s $70 million if you’re wondering). Digital data is like that. When looked at one piece at a time, it seems very insignificant. But put every bit of data you can together and its collective impact is increasingly significant across any conceivable business operation—sales attribution being one.
How are product recommendation engines playing a big part in being able to exact the who, what, when and where of a sale to give credit where credit is due? These engines enable companies to break the customer journey down into individual “pennies” of knowledge. These pennies can then be broken down into smaller and smaller digital data points that allow strategists to understand the impact of marketing and sales efforts with more mathematic certainty than ever before.
Recommendation Engines are taking the mess out of multi-channel
When the customer journey resembled something more linear, it was easier to see what efforts impacted sales. Not too long ago, it worked like this: A promo came in the mail, the consumer handed it to the cashier, and it became part of the “evidence” of what drove the sale for the store. Now that this kind of promotional “evidence” has turned digital, it’s everywhere--making it seemingly impossible to pin down just what made the sale:
Perhaps it was the coupon code the consumer collected on social media that prompted them to finally buy. Then again, they did leave the site a few times and came back through mobile. Maybe it was the geo-targeted shipping promotion that finally convinced them?
Without the ability to tag and track these different digital moments, giving credit where credit is due when it comes to sales attribution is fairly fuzzy. But when you are able to mark these moments and turn them into data via a multi-platform recommendation solution, everything becomes quite clear. With increasingly relevant digital data in hand, sales attribution models become more exacting in terms of pinpointing, analyzing and attributing correctly to the sales and marketing efforts driving each conversion. The multi-channel attribution model is itself taking off.
One data scientist in this recent article on attribution modeling said, “Multi-channel attribution can help us understand why people buy from us, what happens before they buy, what prompts them to make purchase decisions or complete predefined goals, and ultimately determine the most effective digital channels for investment.”
Making a multi-channel campaign management a whole lot easier.
Not only can the artifacts related to recommendation engines add precision in sales attribution across platforms for different conversion activities, they can help manage the enormous effort involved with managing multi-platform campaigns. Everything from what stage of the buying process a consumer was in, where they clicked, what they said and when they made their ultimate decision can be measured and tested to help deliver better sales attribution across the funnel and improve marketing performance channel wide.