E-commerce websites have the ability to collect a ridiculous amount of information.
With access to such large data sets it’s no wonder so many e-commerce businesses are touting about how “big data” and “business intelligence” (“BI”) sets them apart from the pack.
We certainly agree that the insights that can be generated from data represent a significant opportunity for a business of any size, but for all the focus on the systems, tools, and resources, the other side of BI needs just as much consideration – that is, what are you doing with the information?
The ability to answer this question is what turns the data from numbers into actionable insights. While there is no shortage to the questions and solutions that data can provide, BI gets discussed in such general terms that it’s not always clear how one takes data and uses it to make actionable recommendations. So, how do you turn data into actionable insights?
First you need to find patterns in your data.
A year has a lot of data points, so start with peak sales period(s). Look at your data, including the age and gender shopping on your website during that key period, it can offer meaningful insights that can be actioned to improve performance over that period. We’ve found that there are patterns to indicate when certain customers tend to shop over others.
For instance, we found that on certain key dates male customers aged 18-24 were more likely to go online and shop than other days. We also found that during other key dates in the period, woman aged above 35 years were shopping on certain dates but what they were purchasing on those dates were largely for younger males, suggesting that they were purchasing on behalf of their children.
With this data in hand, we then determined what our key objectives were for this period to best identify how to tailor a marketing plan to achieve them; looking to grow revenue so we used the data to launch specific marketing campaigns aimed at increasing conversion rates, capturing new customers and recovering customers who had not purchased for over 360 days. With these goals in hand, we identified specific products that were likely to resonate by customer type before creating multiple creative assets with the product, tone of voice, message/visuals all geared towards each specific customer type. Using these assets to place them in emails and media where each of these customer sets were most likely to view it in order to maximise the exposure, click through rate and conversion. And finally, we launched look-a-like campaigns across social media to capture new customers.
While this data was used to find ways to enhance key sales periods from a marketing perspective, the insights can be used to make decisions from as early on as setting the season’s buying plan. Setting a buying plan that considers what cross-sell opportunities exist to the shopper at hand – without compromising the brand proposition – would provide an even stronger opportunity to maximise sales. Simply knowing who you are buying for and what dates they are likely to buy let you both target your messaging and curate what you’re selling to meet that customers’ needs.
The specific implementation is an important component – what, when and where you are advertising needs to be aligned with the insights you’ve found and the assortment aligned to your customer, but these serve as examples into how you can use your data to action meaningful change to your marketing and buying initiatives.