Retail is an odd topic for this blog but I have a part time job. Interestingly, besides the fact you can make $20 – 25 per hour in ways I will not reveal, stores are stuck using comps and outdated mechanisms to determine success. In other words, mid-level managers are stuck in the dark ages.
Comps are horrible in multiple ways:
- they fail to take into account the current corporate climate
- they refuse to take into account sales from a previous year
- they fail to take into account shortages in supply, price increases, and other factors
- they are generally inaccurate and about as useful as the customer rating scale recently proven ineffective
- an entire book worth of problems
Take into account a store in a chain where business is down 10.5 percent, that just lost a major sponsor, and recently saw a relatively poor general manager create staffing and customer service issues. Comps do not take into consideration any of these factors.
There are much better ways to examine whether specific practices are providing useful results and whether a store is gaining ground, remaining the same, or giving up.
Time Series Analysis
Time series analysis is a much more capable tool in retail. Stock investors already perform this type of analysis to predict when a chain will succeed. Why can’t the mid-level management receive the same information?
A time series analysis is climate driven. It allows managers to predict what sales should be for a given day and time frame and then examine whether that day was an anomaly.
One area where retail fails is in variable selection. Just accounting for sales is really not enough to make a good prediction.
Stores should consider:
- the day of the week
- the month
- whether the day was special (e.g. sponsored football game, holiday)
- price of goods and deltas for the price of goods
- price of raw materials and the price of raw materials
- general demand
- types of products being offered
- any shortage of raw material
- any shortage of staff
Better Linear Regression Based Decision Making
Unfortunately, data collection is often poor in the retail space. A company may keep track of comps and sales without using any other relevant variables or information. The company may not even store information beyond a certain time frame.
In this instance, powerful tools such as the venerable LSTM based neural network may not be feasible. However, it may be possible to use a linear regression model to predict sales.
Linear regression models are useful in both predicting sales and determining the number of standard deviations the actual result was from the reported result. Anyone with a passing grade and an undergraduate level of mathematics learned to create a solid model and trim variables for the most accurate results using more than intuition.
Still, such models do not change based on prior performance. They also require keep track of more variables than just sales data to be most accurate.
Even more problematic is the use of multiple factorizable variables. Using too many factorized variables will lead to poorly performing models. Poorly performing models lead to inappropriate decisions. Inappropriate decisions will destroy your company.
Power Up Through LSTMS
LSTMS are powerful devices capable of tracking variables over time while avoiding much of the factorization problem. Through a Bayesian approach, they predict information based on events from the past.
These models take into account patterns over time and are influenced by events from a previous day. They are useful in the same way as regression analysis but are impacted by current results.
Being Bayesian, an LSTM can be built in chunks and updated in real time, providing less need for maintenance and increasingly better performance.
Marketing Use Case as an Example
Predictive analytics and reporting are extremely useful in developing a marketing strategy, something often overlooked today.
By combining predictive algorithms with sales, promotions, and strategies, it is possible to ascertain whether there was an actual impact from using an algorithm. For instance, did a certain promotion generate more revenue or sales?
These questions posed over time (more than 32 days would be best), can prove the effectiveness of a program. They can reveal where to advertise to, how to advertise, and where to place the creative efforts of marketing and sales to best generate revenue.
When managers are given effective graphics and explanations for numbers based on these algorithms, they gain the power to determine optimal marketing plans. Remember, there is a reason business and marketing are considered a little scientific.
Comps suck. Stop using them to gauge success. They are illogical oddities from an era where money was easy and simple changes brought in revenue (pre 2008).
Companies should look to analytics and data science to drive sales and prove their results.