You Don't Need More Data for Analytics
You need the right data
You need the right data
The trend right now in Analytics is to collect more, MORE, MORE data in order to improve marketing efforts and optimize websites, but what if MORE isn’t the solution? How can we take the idea of demanding and gathering MORE data and make it work for us? The devil is in the details. While marketing blogs and analytics vendors are yelling ‘more!’ what we really need is well-rounded data.
Think of getting into college; you can’t get to Harvard just on 36s or 1600s (are they back to the 1600 scale?) on your ACT or SATs, you have to be involved in the community, have strong relationships, foster trust and demonstrate skill. Your analytics have to do the same. So just how do you make MORE data more than just ‘more’?
We have to get beyond simple metrics. It’s not just the quantity of data collected that will help us improve our brands, it’s the quality-- as in literal qualitative data. In order to achieve the next level of customer insight, we need to rethink the connections and underlying actions among data.
Neuromarketing sounds like science fiction, but it really dives down into the consumers' psychological and emotional response to marketing stimuli and how that drives judgment and decision making. The core ideas here focus on creating a connection to consumers using a system of attention, rewards, loyalty, and retention.
For example, creating a story that highlights a problem they have and then offering a solution, or use of a social meme, builds rapport and a stronger relationship with a customer than a simple product placement would. Neuromarketing is also behind the idea that escalating and intermittent rewards creates a habit pattern, and when customers are rewarded, they attribute that release of dopamine and blissful feeling to your company. This might seem like trickery, but it’s really using the power of human emotional response to work for you instead of against you. Collecting the data against these emotion-heavy marketing tools can help you analyze and realize which sentiments (those that inspire loyalty or reward or reciprocity) lead to better ROI.
Analytics is hard-pressed to acquire the strict, statistical data that we require to generate highly accurate assumptions. In reality, we most often obtain what we can, realizing that we can’t have true control groups and single variables in the real world. (Which is why inter-company siloed data is another problem- but that's for another blog.) Where we CAN exert more control is with textual data, or other forms of research that isn’t passive data collection. I’m talking focus groups, test and control groups, A/B testing...all that wonderful old-skool market research!
In these settings, we not only get more control, but more context for actions. The questions to be asked in these textual data discovery sessions should focused around what questions mainline analytics can't answer. By collecting this kind of data, you’re adding to your data store, but you’re also filling in the gaps between what you already have.
Knowing the customer’s emotional state when performing traceable actions is the next step in measuring analytics and leads to stronger assumptions about customer tendencies, reasoning, and habits. The idea of ‘expressed consumer intent’ captures the idea of learning about what customers are planning to do. Part of this is taking a combined look at customer actions in a way to better infer their emotional state.
If a customer returns again and again to a product page, one could infer that they’re researching products, shopping around, or intend to buy but with no specific purchase date. BUT- if your analytics also track that the same person has added that item to a wish list, or set up a price alert for it on an aggregator site, you now learn that your customer really does want your product, they just want it at a lower price and they’re content to wait to have it until it goes on sale.
Now imagine that you learn through analytics that the wish list they added it to was their wedding registry, for a wedding taking place 3 months from now. This creates a whole different set of emotional assumptions. Regardless of which scenario happens, can you see how this affects your marketing toward this person?
By collecting this customer intention data, one can then more finely target customer segments with the end goal being more total conversions.
This emotional component can also be focused internally. Analytics experts strive for an empirical eye when reviewing data, but it must be acknowledged that biases occur. Collaborating with different teams to review data and share information can be extremely beneficial to mitigating this effect. By collaborating, teams are admitting that their biases may exist and that other eyes might see something they don’t. By acknowledging biases and collaborating, you’re creating empathy within your company and your team, allowing a better internal emotional response to translate to stronger analytics and better optimization.
The heart of marketing- the one thing that hasn’t and won’t change, even when we get to Minority Report-levels of personally targeted content- is the human connection, and we have to keep that a part of our analytical process to better discover the driver behind consumer behavior and purchases. It’s comforting to me, a marketer, to know that despite all the trackable tools and connected Things out there, some of the most interesting and useful tools today in the marketing game to capture data and facilitate better analytics are the more emotional, qualitative ones. Because we are all human, after all.
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