Why Data-Driven Retail Matters
Big data is the driving factor propelling online retail forward. The term has admittedly been over-used, as it’s often portrayed as a cure for all that ails the industry. But if you think big data is all hype, you need to realize that it’s a necessity in order to keep up and improve profit and revenue.
In fact, 65% of businesses report that an increasing number of their decisions are based on hard analytic information. While data does have the potential to make a big impact, it’s important to be clear about what is necessary when collecting and analyzing it to make this ongoing process more effective.
Pricing is inherently difficult in online retail because by nature the industry moves at the speed of light. Competitors lurk around every virtual corner, targeting your customers with ads promising lower prices or perks you don’t offer.
It’s because of this competitive nature that online retailers both need access to this data and a clear strategy to make it actionable and impactful.
Data can improve retail regardless of the channel, but it’s especially important online. Competitor and historical data tell retailers exactly how effective their previous pricing was.
When these two types of data are combined, it’s possible to get a full view of where you stand both in the past and in real-time.
And pricing is just one use for data, assortment is another realm where having a good understanding of where competitors stand and what has sold well in the past helps online retailers order the right products and reorder at the right time.
A recent study by Gartner predicts that up to 33% of merchandising personnel at top 10 retailers will no longer be needed due to the rise of algorithm based decision making.
From assortment to pricing to marketing, algorithms and the data they produce are truly the future of online retail.
Terms Needed for Data-Driven Retail Success
There are three terms that are imperative to understanding data’s applications in online retail. They are: data mining, data analytics, and data-driven decision making. In order to get the most out of data, retailers need to start mining it first.
This allows them to collect vast quantities of data to find patterns and outliers to make more accurate predictions based on past events. Make sure your data is clean, as 27% of IBM survey respondents didn’t know exactly how clean their data was. This is a widespread issue, as bad data quality has a $3.1 trillion impact on the US economy each year.
Data mining leads to a treasure trove for online retailers that they can easily act on. Brick and mortar retailers often need more time to roll out price changes based on data because it often requires changing price tags.
This can be updated in near real-time for retailers with online channels based on data analytics. Data analytics entails looking into big data to find actionable insights that will drive value.
It is certainly worth the effort because improved data quality can influence a 15-20% increase in revenue. This leads to data-driven decision making, which uses mined historical data to improve key business metrics. When customer data is used in personalized advertising, average online cart size can jump 10-15%.
So how can online retailers make it happen?
Six Steps of a Data-Driven Retail Strategy
After understanding the fundamentals of data, there are six steps, much like the scientific method, to follow in order to create a data-driven retail strategy:
1. Ask a question: This is where you set your intentions. What do you want to learn about? Be as specific as possible to focus your outcome.
2. Do your research: Based on competitor pricing data, ask more targeted questions.
3. Make a hypothesis: After understanding the state of your data, as well as competitors’, make an educated guess about the outcome.
4. Conduct an experiment: Test your hypothesis. In online retail, price tests are a great way to try out new pricing strategies to see the impact on margins and sales.
5. Analyze the results: Did you get the outcome you expected? If so, why? If not, why not?
6. Retest: Data-driven retail strategies require on-going attention. Customer and competitor data changes all the time, so keeping up with it is required to remain accurate.
These six steps are the bedrock of using data in retail. The saying “always be testing” is more true now than ever before. The hyper-competitive nature of online retail means that nothing is ever fully optimized, so you’ll need to keep striving for better conversions, revenue, and margins.