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Decision making for retailers and CPGs has changed tremendously as the amount of data available and the processing power of computers has increased. We’ll look at the factors contributing to this change, as well as insights into what this means on a practical level and what the future may hold.
How the data collection process is changing
Perhaps most importantly, omnichannel data can now be collected and analyzed that looks at the path to purchase for both online and physical sales. This data provides rich insights into macro trends like how users behave, what signals they respond to, and what the overall purchasing cycle looks like; as well as micro trends such as what products are purchased together.
What is also interesting for those studying this market, is the move from analyzing data generated inside the organization, to including data sources from external sources. This move really opens up the potential data possibilities, for example allowing organizations to gain visibility into what happens to their customers when they are not directly involved in their particular store, and allowing the “completion” of the overall picture by filling in gaps in data that wasn’t available internally.
This shift has had a real-world impact: smart companies are now looking for insights, rather than just raw data – to not just inform retailers what happened, but more importantly what actions are needed in order to improve.
Possibilities and limitations when analyzing consumer data online and offline
Unless you’re Amazon or eBay, you probably only have access to at most 1-5% of what goes on online. So if you’re relying on internal data, you’re missing out 95% of the picture.
What happens, for example, when a consumer leaves your store, and the step after that, and so on. This analysis of the customer journey is critical to business success, particularly when it comes to ecommerce. Other relevant questions need a fuller, richer data picture: What drives shopping intent? What is trending? What is influencing decisions your customers are making?
This will influence your strategy and tactics. For a real-world example, take the same search for “501”, on Amazon and on Target.com. On Amazon, you get the very relevant result of Levi’s. On Target, however, the results are a lot less relevant, showing that Amazon has leveraged their data to provide more relevant results and assist the customer in purchasing what they are looking for. As a richer data picture is built for a particular consumer, which takes into account the user’s journey and path-to-purchase, the retailer can already guess what the shopper is looking for and make it as easy as possible for the consumer to get what they need.
This does have its limitations, however. Privacy concerns, and particularly the recent requirements of GDPR, affect how a marketer can target the customer. On one hand, knowing what the consumer wants enables us to help them; on the other, privacy concerns especially regarding data gathered by our systems, make this a tricky area.
Tech advances in data processing and analysis
As mentioned in the introduction, advances in technology, particularly in processing, have enabled analysis on a whole new level.
Using product classifications, and automatically classifying a product to a category for millions of products, allows us to understand that a product is the same over different ecommerce stores, and enables us to analyze, track, and compare this.
A good example is in the fashion world. Using image recognition, a pair of blue jeans in 2 different stores can be compared, even if the brand is not the same. This allows the comparing of related factors like colors, designs, and pricing.
How Retailers And CPGs Use Data Analysis To Make Business Decisions – From Revenue Management To Pricing
Retailers and CPGs are leveraging this unprecedented amount of data to analyze key metrics relevant to their businesses.
They are able to understand their category market share, accurately keeping track of where they stand in relation to competitors for each category and assessing the impact of changes such as advertising campaigns; They are able to see their share of traffic for each category, and optimize this while dealing with any exceptions; they can understand and assess which products are price sensitive and by how much, enabling the CPGs and brands to make better short- and long-term decisions when it comes to pricing, and they can investigate the reasons behind low performing categories or products, and how to rehabilitate and optimize these.
Finding The Balance Between Data-driven Decisions And Human Know-how
With the explosion of data available, and the means to analyze this data, finding the balance between human decision making (including drawing on experience and “gut feel”) and data-driven decision making can be complex.
Some decisions can be 100% automated, for example, dynamic pricing. This can be set up to offer a price to the consumer that takes into account a number of factors such as the consumer’s history and behavior, the amount of stock available and what competitors are offering. For most organizations, using humans to keep track of this pricing across millions of products and thousands of factors, in real-time, is impossible.
Other decisions require the machine to identify the issue and use data to provide a suggested action for the target team. Examples of this would be sourcing or finding the right affiliation channel.
A great example of this combined approach is when Amazon leveraged data and human input to ensure that eclipse glasses were the No. 1 product last summer during the solar eclipse.
How Retailers Can Generate New Scalable Revenue Streams Monetizing Data
Even the actual data that retailers are gathering is valuable. With everyone realizing that in order to succeed, one must have access to as much data as possible, retailers are seeing the value of the data they possess.
Smart retailers are even building marketplaces that can utilize data to provide services to their sellers, such as Amazon vendor central. This data can be pooled to provide an unrivalled picture of what is happening in the ecommerce ecosystem.
Data As An Overused Buzzword
There is a lot of data out there, but if it’s not used correctly, it is not really a competitive advantage.
Data has become in a sense an overused buzzword, especially by people and organizations that don’t utilize it well. Data is a raw material with similar characteristics to other raw materials – on its own, it’s of limited value, it has to be processed and refined into something useful.
It’s more appropriate to talk about insights. Many retailers and brands are drowning with data that they have no idea what to do with, yet this is exactly where the opportunity lies. We need to stop talking about data and start talking about insights. Data is a commodity today, but it forms a critical part of the solution.
Using Data To Drive Insights Like the Pro’s
At Market Beyond, we appreciate that data isn’t enough; companies need to see the full picture of consumer journeys and macro and micro trends in the ecommerce ecosystem, but most importantly, they need insights; actionable, real-time insights.
A company that is hitting it out the park in this respect, and one that we admire, is eBay. They are doing it right by looking to use inside data and complementing it with external data – mixing it in ways that give them a business edge and the ability to keep amazing traffic levels with excellent conversion rates. In fact, more than 25% of ecommerce traffic in the US goes through eBay.
So if you also want to do it right, get in touch, and together we can ensure you’re optimizing your inside data, getting the right external data, getting critical business insights, and reaching new levels of success.