Double digit growth, Amazon rising as a superpower, and voice-based ordering becoming the new normal. Haven’t we been hearing this all too often? But apart from the ease of use advantage, the single biggest factor differentiating ecommerce from offline retail, is the versatility of analytics tools being used, especially product analytics to derive valuable cognizance of customer’s behavior and operational procedures.
From around $1.9 trillion in ecommerce sales this year, the figure is expected to reach $4 trillion by 2020, which is going to hinge on well-selling products that ecommerce firms can capitalize on, while identifying less popular products that need a turnaround.
Product analytics is that niche tool you could add to your arsenal of measurement metrics. But a word of consideration before we proceed: Your ecommerce analytics teams, business and tech teams need to work like a smoothly oiled machine, learning and building on each other’s insights. Let’s dive into key product analytics to get your sales curve back on track.
Inventory and Order Analytics
Product analytics breaks down your inventory and order performance to give you valuable data to plan your supply chain strategy for the next fiscal. Deep learning algorithms that work on historical and recent data going into these analytics systems now even suggest the next best thing to do.
This could be like reducing the quantity of products that aren’t expected to be in the festive product mix, or an expected surge in new smartphones making waves, in the next Black Friday type of mega sale. That’s inventory planning.
Order analytics shows not only the positive side of your e-selling, but highlights canceled orders or orders that didn’t make it all the way through your purchase funnel.
Facebook, for example, recently started expanding the used car inventory on its platform, based on the huge dealer data set it acquired and its own targe customer data.
They used a mix of inventory and product analytics to understand the demand in different areas, to strategically tie up with additional major auto dealerships, thereby becoming a used car selling platform in itself.
Use these metrics to take more informed decisions, be it with your suppliers or with your internal teams, to arbitrate on balancing inventory and spiking orders.
Customer & demographic Analytics
Get distracted for a second, and you stand to lose a lot in this ultra-competitive space. Because you don’t want to lose your prospects’ attention. But if you are able to ‘distract’ the latter towards you with offers (you get what we mean..), then it’s time to turn up the lights and rake in the money.
Product analytics for ecommerce will help you know which customer groups are purchasing what categories of your products, and why. Yes, why too. Case in point: Airbnb has a vast repository of customer and demographic data, something even the world’s biggest hotelier would kill to get a peek at.
Through a revolutionary concept of renting out home spaces that has got the world out on the road, Airbnb’s analytics team now offers Market Minder, a market intelligence tool for hosts to understand customer behavior in the vicinity, and compete against hotels looking to convert the same customer. Ecommerce analytics for products now even lets you identify the demographic attributes of your purchasers, broken down by country, region, gender and age group.
Revenue & Profit Analytics
If you are reading this and have added your hand into the e-commerce bowl, then it won’t be a surprise being aware of ecommerce platforms like Shopify and Magento. Such platforms have allowed thousands of businesses all over the world to take their selling online, and allow for product discovery across their regional reach.
Gartner estimates this ecommerce platform market will grow at a 15% CAGR through 2020, helping both B2B and B2C sales. This is just one example of the power such platforms have, leading to bespoke revenue and profit product analytics solutions. The kind of insights could include the average revenue previously seen during every customer visit vis-à-vis current revenue generated per visit.
What’s more, retail analytics solutions show you your path to reaching revenue and profit targets set, and in case you’re not set to reach the numbers, you have the data to know which customer will buy what. This is based on his buying history, and his propensity to buy certain products which can be upsold, based on his browsing history in previous product searches.
Lifetime Value Analytics
If you look at the kind of brand value every product purchased adds to customer group, these are the feeders to estimate the overall lifetime value you are generating for your customer. Netflix used ecommerce analytics to its customers watching its shows, and found that those who loved House of Cards were either Kevin Spacey fans, or watch films directed by David Fincher.
This is not surprising, if you think so, that external data could be referenced to arrive at a result. They can now source databases of fans from both the stars, and target relevant shows and boast of conversions even before interest is evinced. If this is a live case of product analytics driving not just your short-term KPIs forward, systems can now show how each product or category group contributes, also called average customer lifetime value contribution.
Less lifetime value categories can be analysed to know how to adapt selling strategies and get sales momentum going again. Even in the B2B ecommerce domain, Forrester predicts that by 2020, ecommerce sales will breeze past $1.1 trillion, and comprise almost 12% of global B2B sales.
Channel Source & Location Analytics
Recent data by Google Analytics report that about 75% of product conversions happen through multiple digital touch points, or various channels.
A customer could start his search for a leather belt through his mobile browser, then reach home and find the best price by checking prices on various portals. Finally, seeing an offer on the Alibaba app, he purchases through the app. Now a zoomed in view of this behavior from an individual or a product group point of view through a retail analytic solutions throws up interesting findings.
Marketers could find and investigate the rationale behind specific product behavior, and could find out the person’s frequented shopping districts, or coffee shops, and work to provide targeted offers. It could be surprising that such results could be contrary to the results from a zoomed-out view of the buying history, thanks to product analytics tools.
Tracking tools like TrackStreet, which are already on a funding path, help companies amplify their expertise of tracking channel and location data, in turn providing offers that delight their customers.
Offline retailers are going online to increase their reach, and online retailers are going offline, for the same reasons. But at the center of it all is their quest to improve their level of gaining insights into as much customer and product data they can gather.
The B2B ecommerce market is expected to touch $6.7 trillion in sales, comprising 27% of global trade in manufacturing, and the secret behind the astounding rise is incorporation of tools like product analytics.
With customer’s evolving tastes, and the increasing scope of manufacturing to create customized products, the essence to use product retail analytics solutions cannot be stressed more. It’s now a given that your tech stack for your ecommerce business must have this incorporated in some form, for you to invest resource where it matters the most.