Price Optimization is not the same as Price Monitoring and Price Matching. Price Optimization is price setting by the Willingness To Pay (WTP) of customers. A breakthrough in Consumer Behavior market science and eCommerce Big Data means that Price Optimization is now available to every industry that sells online.
Sellers must always be thinking “Multi-Product Pricing”. Today, when sellers discount a product, they rarely consider the impact on the rest of the category or portfolio. Yet the cannibalization is more destructive to profitability than the fiercest competitive rival. Modern market science now makes it possible to optimize prices across your whole portfolio. Profitability can be boosted by 10%, 20% or even 30% without sacrificing market share. The bigger and more complicated the portfolio, the greater the profit potential.
The myth of the “long tail” encourages sellers to ever inflate their product assortment in the hope of increasing market share, revenue, and profitability. But even if inventory costs were zero, sellers would be better off trimming their product assortments rather than inflating them.
Assortment Optimization is an extremely complicated problem to solve. Change your assortment and your customers will change their buying behavior in unexpected ways. Market Science is only now developing the tools that can optimize which assortment will maximize profitability.
Conjoint Analysis gives everybody from product/brand managers to senior executives detailed insights into customers, products, and competitors. The results can define strategies and successfully guide companies through treacherous markets.
And yet Conjoint Analysis has been held back from fulfilling its full potential.
Data driven companies should be using this market science daily in each of their product categories. Yet big companies run, on average, about one Conjoint Analysis study every year.
The obstacle limiting Conjoint Analysis is the customer survey. The surveys are expensive to develop, slow to administer, and narrow in their scope.
Clickstream Conjoint eliminates this obstacle. The attributes listed in the online product catalog replace the customer survey. And the online customer clickstream replaces the test subjects.
Now real-time Conjoint Analysis can personalize shopping, set dynamic pricing, and automate new product design.
Who is your biggest competitor? It’s you!
You are your own worst enemy. Badly aligned prices within your product lines are hurting you a lot more than your competitors. And you already knew that matching your competitor’s discounted price is not your best strategy (but you probably do it anyway).
Why? Because internal cannibalization can outweigh the loss to your most vicious rivals.
Good news! There are new tools, like the Hurricane Chart, now available to help you reduce cannibalization and lift your profit (by up to 20%).
Many attempts have been made to plot Customer Value and Willingness To Pay (WTP) against Price. The Value Equivalence Line (VEL) and the Fair Value Line (FVL) are two leading examples.
When done well, these “Value Maps” become a powerful strategic tool that can be used to maximize market share, revenue, and profitability, as well as identify “Blue Ocean” opportunities for new product innovation.
But until recently, Value Maps were ignored by marketers and executives. While good in theory, the required data was nearly impossible to come by. The concept of “Customer Value” was still abstract, and there was no quick and reliable way to calculate Willingness To Pay (WTP).
The breakthrough came with eCommerce AI. Computers endowed with Artificial Intelligence are now able to take commonly available eCommerce data and calculate Customer Willingness To Pay (WTP) in real-time.
Today, strategic companies can use WTP Value Maps to precisely navigate the Hyper-Competitive landscape of modern business.
eCommerce AI is a breakthrough in market science. It goes much deeper than product search and purchase trends to reveal the underlying secrets of what customers want. AI explains why customers make purchase decisions, and AI predicts how changes in the market will impact sales. eCommerce AI allow you to optimize category prices, manage your product assortment, streamline your inventory, and identify opportunities to introduce new products.
Since Adam Smith wrote “The Wealth of Nations” in 1776 surprisingly little progress has been made in our understanding of competition. Philosophers and academic researchers have left the field open to strategic management consultants: the twentieth-century equivalent of alchemists.
But like all scientific research that is led solely by the desire to solve real-world problems, the solutions were half-baked. Valuable tools were created, but we still didn’t know why they worked.
With the arrival of the twenty-first-century, these strategic alchemists are being replaced with market science and artificial intelligence. The data and analytics are finally available to fill in the gaps.
This transitional period is a good time to review some frameworks of the past and compare them with the emerging scientific strategy tools of the future.
Hint: It was invented in England in 1890
What is it? It’s the Demand Curve (price versus quantity).
Chinese eCommerce stores use the Demand Curve to increase Revenue and Maximize Profit.
China is now the world’s most sophisticated eCommerce center in the world. And China has invested heavily in Market Simulation and eCommerce AI to drive sales and increase revenue and profit.
These Big Data tools are now available for anybody to use.
When a thousand other stores sell the same products online then optimization becomes critical.
China’s hyper-competitive eCommerce environment forced rapid evolution of the Big Data Analytic tools required to compete. China quickly adopted leading-edge market science to leapfrog the rest of the world.
Today, Chinese eCommerce stores of any size can calculate the Willingness To Pay (WTP) of their customers from only 30 days of data. From this, they can instantly generate full Demand Curves and find the profit maximizing price for each of their products.
Re-pricing complicated multi-product portfolios boost profits by over 20% while preserving market share. Same-store cannibalization has been eliminated. And when competitors discount, Chinese sellers don’t blindly match prices like their western counterparts – they calculate their “Best Response” to protect revenue without sacrificing margin and accelerating a price war.
eCommerce today suffers from Hyper-Competition. Thousands of vendors are all fighting to be the cheapest. As a result, most of the US$22-Trillion eCommerce industry is bleeding money with negative profit margins.
Winning vendors are, in fact, math companies! They use the most advanced analytics to find revenue opportunities. Winning vendors use Hyper-Analytics to overcome the Hyper-Competition and become profitable.
1.7 million viewers watched this live streaming interview at “Startup Shanghai” with Ted Hartnell on the subject of eCommerce Optimization.
Ted Hartnell has been researching price optimization, a strategy that converts big data into profits for retailers on online shopping platforms, since he was a graduate student.
“China has open e-commerce data and the world’s most hyper-competitive e-commerce environment. We want to be part of it, and our technology can help online retailers compete and win”.