Price setting: some well-understand price-setting methods and how can data analysis help improve the price strategy
The price strategy is essential in every industry. Price is one factor in the marketing textbook, and it will affect company margin significantly. Factors such as quantity, quality, delivery expectation, and so on may influence the price setting, which is why price analysis is essential in the real business world. This article will introduce some standard price-setting methods and touch-based on how analytics can help with price setting. I won't cover the algorithm but will discuss what features we may consider in the price model.
So what are the typical price models?
Cost-based
This method is quite usual in traditional industries. The principle is to add an extra % on top of the cost. And there is room for promotion or bundle selling from the additional %. This is a promotion-driven price method; it will correlate to the margin in the financial reports. Data analysis can help provide a simple what-if model to calculate the margin according to different marketing strategies.
valuee-added
This model is mainly for bundle selling. For example, you want to buy a vehicle, and there are some more options that you can add to your car. Those selections are value-added items. The other example is the premium member. You can have additional benefits once you upgrade to a premium member compared to the basic ones. The core of this model is to set a suitable anchor. And data analysis can help to find the anchor of basic price setting. The anchor price will affect how people decide to add, upgrade, or give up. The anchor setting should refer to market investigation and competitor analysis.
competitor comparison
This is typically how new products try to penetrate a market occupied by many players. I would say this method is used together with other methods. For example, you set your price based on the cost and additional 40%, but you observe that those competitors that sell similar products only offer the price with 20%+cost. Then it would be best if you decided on ways to fight, either adjust the price, bundle new items, or emphasize the unique points of your products. You need to take R&D ability, funding on holds, and market potentials into consideration before you join the price competition with those players. Analysts play an essential role in analyzing competitors' backgrounds, fundings, etc.
demand-based price
The previous three methods only consider the dimension of our products. Now we need to discuss more dimensions of the fundamental business challenges.
- demand-supply perspective
We sell products strongly rely on supply support. The supply may rise at a high peak in certain seasons or holidays. The supply plan may be affected by politics, disease, or strike, and it is the time data analysis stands out to present its value. A suggested solution is a dynamic model considering features such as 'procurement expenses,' 'inventory costs,' 'demand cannibalization between particular products,' 'competitor prices,' and 'promo activities (may increase the volume).' A suggestion here is that using segmentation instead of each raw data to build a model is better. (like we don't want to overfit our model or bad ROI)
2. Price discrimination-customer segmentation perspective
The same product may be at different prices in different channels. This phenomenon is quite apparent in the E-commerce industry or B2B2C model. The core concept here is that not all users act the same. Some users may only stick on some channels, and not all the users will do price searches on the internet. So the data analysis can help understand a. Customer segmentation based on customer features/behaviors, RFM, omnichannel behaviors b.cahnnel cost to set prices accordingly, differentiate the price and maximize the profits.
3. product status perspectives
Products with different shelf-life, package sizes, and quality will have different values. Hence we need to differentiate the prices. The data analysis can help categorize those factors into assigned groups, take groups as features into the price model, and use the information to support price strategies.
This article is quite a rough introduction to price strategies and how data analysis can help there. Various industries have various features, customer behaviors, product status, and supply situations. It isn't easy to find a way to explain all in short, but the principles are
a. understand you cost-margin structure
b. set the anchor as a base
c. do competitor analysis
d. do channel analysis
e. consolidate the domain knowhow of supply, customer behavior, and product status
Price analysis is a dynamic and long way to go, and we, as data analysts, can contribute significantly to it.
