Our co-founder attended this year’s IRCE which was held in Chicago. For those who aren’t aware of IRCE, it’s one of the biggest retailing conferences. In his 2 days, 120 meetings and interactions with 500+ companies, he made a lot of discoveries about the state of e-commerce and retail data in the current scenario. The companies he met with were mostly tech services, products and logistics companies that sell to e-commerce and retail companies.
Their biggest problem while acquiring leads from data providers is that it lacks segmentation.
Most data providers get you a list of retail companies and not e-commerce because the data providers have not yet woken up to the fact e-commerce is the fastest growing model within retail. Even if some of them provide data of e-commerce companies, the lack of insights specific to e-commerce makes it a lot less valuable than it otherwise could have been.
So customers resort to manual research, guesstimates and vague rules of thumb – for example, to estimate SKU size, Shipment volumes, Shipping policies etc.
It doesn’t end here.
Most companies go for a third data source to find out which of their competitors’ products the prospects are already using.
The whole idea behind buying data is to enable context-based selling. Data or insights derived from data should inform the sales rep about the context.
The data-hunt doesn’t end there. How do the e-commerce & retail companies manage warehouses? What about their shipping? These companies go for a fourth data source, to know what’s happening behind the scenes.
Contextual information for someone selling to e-commerce customers would have to glean from the following questions – Is the e-commerce lead B2B or B2C? Or is it P2P (Peer to Peer)? Is it auction-based? What’s their channel strategy? How many SKUs do they have? What’s their shipping geographic coverage like? Is it international? Cross-border? What shipping APIs do they integrate with? What about their average order value (AOV)?
Companies selling shipping services to e-commerce and retail verticals struggle with dimensions and weight of SKUs. Package size for any given SKU can’t be automatically found. It comes with experience for a few shipping companies. USPS introduced flat rate boxes in a few standard sizes (S, M, L, etc.) to add a predictability quotient to the dimension factor in shipping. It becomes easy to predict dimensions when shipping companies know it is 100 L(large) and 1000 M(medium) flat rate boxes. But, weight remained inestimable.
For some companies, the unboxing experience is their focal point. More than e-commerce marketplaces like Amazon, eBay, etc., small e-commerce enterprises focus on a great packaging and a better unboxing experience. What’s important to the company? How fast does the product reach or what kind of an experience the unboxing gives? This is the kind of research the sales rep selling to e-commerce leads should be doing. Not how many SKUs a company sells.That should be readily available.
Data about a few companies can be easy to come across – like the big names who have been around. There are trade shows and conferences which retailers attend. Some data can be gathered there.
Data science and predictive lead generation can help you generate high-quality leads at scale. SKU, SKU value, business model, AOV, shipping partners, etc. can be aggregated at scale using data science and the leads can be matched to your criteria using predictive modeling.
PipeCandy is using data science techniques like natural language processing, machine learning, and predictive analytics to scale lead generation and replace manual research.
If vertical data (such as insights about e-commerce leads) is readily made available, SDRs can focus on doing what they are hired for – writing compelling and personalized emails / doing very thorough introduction calls, while account executives can focus on closing deals and getting the cash flowing. They don’t have to waste time on researching or making erroneous assumptions.
We are mid-way in the year 2017 and the holiday quarter is around the corner. Catch up on your sales quota backlogs with high-quality leads. Start thinking about predictive lead generation.