If e-retailers get it right, why do so many go out of business?
Mar 09 2015
For instance, Shoppers Stop and others are creating an online presence with no perceptible competitive advantage. When the newly funded websites offer amazing deals on a daily basis and are aggressive in their mailing lists, how do you think an online version of the brick and mortar Shoppers Stop or Lifestyle fare? Do you think this will work for them? I think not.
On the other hand, had these retail chains been focused on future much before the Flipkarts of the world were born, they would have tested their assumptions on growth coming from tier 2 and 3 towns and would have opened up a hub and spoke model to deliver the selection and prices to customers in these towns.
By not adapting what Rita McGrath calls Discovery Driven Planning, these retailers have lost the battle for Horizon 2. The last mile delivery could have been better done by these chains along with an e-commerce site which would have made the Flipkarts to fight the battle purely on price which as we all know cannot last for long. Besides, without the large numbers, they would not have got the huge valuation and funding, either. Perhaps these retailers should now plan for Horizon 3 much before the next wave of change happens.
We are all slowly moving into buying things online. Most of us would have purchased at least an air ticket online by now. Youngsters purchase shoes to shirts and trousers from e-retailers. They go to a shop, check the fit and design, and then order online for the price advantage. Once experienced, they simply take it as a challenge to get the best rates from different sites. What is becoming increasingly clear is that the safety, technology and price sensitivity are all at par for many competing e-retailers. Things have become more and more like brick and mortar business now.
But why is it that such well-financed online shopping sites as jewelskart. com, ezeego1.com, yebhi. com, yepme.com, carwale.com, egghead. com and roxy.com have had such trouble staying in business? Why are so many struggling to survive despite generating impressive visitor count numbers? The trouble lies in abysmal yield-to-buy ratios; for example, Fortune magazine indicated that the automotive sites’ yield was only 0.7 per cent. Without generating referral and transaction revenues, these sites have limited revenue-producing options for their vendors, in this case car dealers, who will increasingly balk at paying listing fees. Eventually consolidation in the online industry will become rampant but this consolidation is only a symptom, not a solution. The same woes plague all the vertical online shopping sites.
The trouble is rooted in classic consumer information-processing practice. Visitors use these sites mostly for information. The sites provide wonderful search mechanisms that allow visitors get listings of the brand possibilities. However, these lists themselves are often too long, leading to indecisiveness and, ultimately, no purchase. The overwhelmed visitor is unable to proceed from the information search stage to the evaluate-and-purchase stage.
I propose a remedy: provide the evaluation. That is, narrow the list of possibilities to identify only the three best bargain opportunities that satisfy the shopper’s criteria; marketeers can do this using the statistical technique regression analysis. The site visitor who has these recommendations will be more confident about making a decision, realising any of the three is a good deal. Furthermore, site visitors can be more motivated to buy if they fear losing out on distinct bargain opportunities if they delay. Not that the managers are not aware of this but when you take money for listing, recommending will be an issue. The other way is to have your revenue model more consumer-centric. Regression analysis is an easily implemented and comprehended technique. Regression examines a dependent variable Y — the prices within a product category — as a function of independent attribute X factors. For the LED TVs’ price analysis, for example, these Xs may include screen size, resolution and warranty; for stock prices, the Ys would include revenues, profitability and debt ratio, perhaps.
Using the database of possibilities that emanate from a site visitor’s specifications, the regression software fits an assessment model or equation to the entire database. The bargains are these brands or items for which price is less than the assessment. Moreover, the assessment equation communicates attribute X coefficients that can be interpreted as the value of the attributes; so for the TV example, one can ascertain the monetary value of each screen inch, each resolution line and each warranty year.
What is inherent here is that unless differentiated strategy is not there for an e-retailer, things will always be messy. The service quality or responsiveness, the convenience with which a customer can do business with them (like Amazon), or different ways of serving the customer quickly are all differentiators that would help drive business.
(The author is CEO and managing director of CustomerLab)