As price transparency increases, there is increasingly downward pressure on prices. The fear of losing volume to a lower-priced competitor inspires price-matching which can lead to a spiral-down effect, and a race to the bottom. Companies that know how to accurately measure customer price response can reverse this trend and find surprising opportunities to get price premiums.
One of the most striking examples is in the competitive retail electric market. A number of states have de-coupled electrical energy generation and transmission from retailing. To date, 24 states have provided some measure of competitive retailing of electricity. These states have created a fascinating laboratory in which customer price response can be measured.
The intensity of the competition was highlighted by the following factors:
- Retail energy is perceived to be a commodity (you can’t differentiate electrons);
- Switching costs are negligible;
- Third-party search engines make pricing completely transparent.
Our client was an electricity retailer who had previously engaged in a “land grab” approach, winning subscribers with low margins. The concept of “share at any cost” was not sustainable, but as discounts were curtailed, share dropped dramatically. The CEO sought us out because of our experience understanding customer response in highly competitive markets.
We analyzed 22 million price quotes which resulted in 8.2 million contracts. It was critical to normalize for non-price variables such as environmental factors, promotions and strategic implications. Customer attributes such as SIC code, tenure, term & usage were analyzed. Only then were we able to understand the attributes that were predictive of price sensitivity and forecast customer willingness-to-pay.
The granular analysis at the micro-market level gave insights that were not available when looking at aggregated data. We identified a significant number of market segments willing to pay $4 per megawatt hour more than average. Since their typical margin was $3 per megawatt hour, that was substantial.
Armed with the insights from this analysis, our client could answer such questions as:
- When should a lower competitor’s price be matched?
- When can a price premium be justified?
- How much should that price premium be?
- When could value-added services justify higher prices?
With this targeted approach, our client proved that it could use the science of predictive analytics to simultaneously grow share and margin, even in a hyper-competitive market.