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The Evolution of the Data Driven Company
Sometimes it seems to those of us on the outside that big corporations with massive data collection and analysis functions just “are”—they always have been. That is of course not true. Every company, regardless of its current size, has had to develop its data capabilities. Even your company today may have some basic data collection and analysis capabilities.
It is useful to create several “phases” of this data evolution. We do this for a few reasons. First, it’s important to have a scale – a growth chart, if you will. This scale lets you know where you stand in relation to organizations that collect and analyze complex data. But we do it for another reason. At different stages of this evolution, firms have different capabilities. We want to categorize these capabilities so that any company – regardless of its stage in the data evolution process – can take advantage of data-driven processes.
Phase 0. Collecting little to no data
Of course, everyone must start at the beginning. Initially, companies did not collect much data on their processes. Every company has a bit of data – basic accounting data is essential to keep up. A Stage 0 company is characterized by two criteria:
1. Stage 0 The company does not have much data beyond payables and receivables and
2. A Stage 0 company recreates data when it is needed to answer a business question, rather than collecting it in advance.
In other words, a Stage 0 company collects little or no data on an ongoing basis. This does not mean that a Stage 0 company does not know its business. Conversely, to be successful any business must have a good understanding of sales cycle times, the types of prospects most interested in buying, the most profitable products or services, and approximately how much they should charge for those products. and service.
But that’s different than asking what the sales cycle times were for the last 5 sales. or the last 50 sales. Or asking what the profit was for the last 50 customers.
Additionally, many companies are able to extract this information from records, entertainment and research. But stage 0 companies have not collected this information in advanceBe prepared to answer such questions before they are asked.
If you identify yourself as a stage 0 company, that’s fine. Don’t worry, you’re in good company. Being a Stage 0 company doesn’t mean there aren’t opportunities for you to leverage data-driven business practices. In fact, this book will outline specific things you can do today to transform your business into a data-driven one.
Phase 1. Basic reporting
Companies that collect data typically report on it. Stage 1 The company generates a basic analysis tool: reports. To be clear, a report is simply a summary of collected data, perhaps some basic statistics behind that data, such as average, sum, minimum, or maximum. These are quite common in most companies: sales activity reports, prospect summary reports, sales projection reports, cash flow reports, production reports, etc. A Stage 1 company, however, is characterized by a lack of formal analysis of these reports. In other words, it is left to humans to interpret these reports.
Now, there is nothing wrong with human interpretation. In fact, humans can see patterns in data that computer programs are not able to detect. An important criterion for a Stage 1 company is that this data has nowhere to go.
Of course, once the data is collected and stored, there are plenty of opportunities. In this book we specifically discuss the next steps that Stage 1 companies can take to use the data they have.
Stage 2. Trending and forecasting
Once sufficient data is available, and the company has the right tools in place, historical data can be used to help spot patterns and potentially predict future outcomes. Stage 2 The company uses their data to predict trends and predict outcomes.
To be clear, these classifications and predictions are done automatically with calculations and processes. It is not enough for a Stage 2 company to rely on human interpretation alone. Stage 2 The company can tell you what the sales forecast for the next week is, with a margin of error for how confident they are in that number. They can tell you how long it takes to process an order or how much time will be spent on service or installation.
To know this, data has to be collected over a reasonable period of time. How long is reasonable? Well, that depends on your industry and the type of business you do, but later in the book we’ll cover some ways you can predict “long enough.”
A stage 2 company is very good at predicting results. They are usually not surprised by regular ups and downs in business, as they have been tracking “normal” for some time now. But as good as a Stage 2 company is at predicting outcomes, it cannot influence them with any regularity. That’s where our next step comes in.
Stage 3. Drawing conclusions and classifying
It’s one thing to know that you’ll sell 100 widgets next week. It’s an entirely different thing to know that if you drop the price by 10%, you’ll sell 150. A Stage 3 company knows this because they use their data to predict relationships and categorize influences.
Predicting relationships requires us to go beyond predicting outcomes and study the inner workings of why things happen. What causes our sales numbers to drop in November? Which customers are likely to pay more for our product? What combination of product, line, and personnel creates the greatest potential for delays in production time?
These questions require us to stack data against other data and see if there is a connection. If over time a Stage 3 company can’t tell you who their most profitable customers are, why not? And they can use that information to find other, more profitable customers.
Stage 3 The company can list the impacts of their results or Key Drivers. These key drivers can influence numerical outcomes, such as profitability, or non-numerical outcomes, such as: Did they buy or not? These key drivers help guide decision making. When a senior leader or executive decides on a stage strategy, Stage 3 companies can use these key drivers to get a sense of how they will react to the new strategy. Because they can, simulation is often a key decision-making tool.
However, humans still use instinct to guide their strategy. True, by having a list of drivers and an idea of how they affect your business, you can simulate different strategies and see how they will work. But can you emulate each strategy to see which one is best?
A simple example will demonstrate how difficult it can be. Let’s say you are a clothing company that makes 3 styles of shirts and 3 styles of pants. The shirt and pants come in 3 different colors and 3 different sizes. You only have one production process for clothing, and you have to decide how much time you want to spend on each style of shirt, pants, color, and size before restarting the next process. And of course, there is a different level of benefit associated with each style of shirt and pant. Oh, and you can’t do enough of everything to meet demand; You have to pick and choose.
Even if you know how many shirts and pants will be sold and at what price, with different combinations of shirts and pants and colors and sizes and the tradeoffs between them, how do you use each combination?
Those with a little math background may recognize this setup for a math problem called “optimization.” This type of problem is regularly solved by our company’s end stage.
Stage 4. Optimizing
Optimization is the idea of getting the most (or least) of whatever outcome you want: usually profit. A Stage 4 company is able to find the maximum or minimum they want by scanning over all possible conditions that could affect their results.
For example, in the scenario above, a Stage 4 company can tell you exactly which shirts, pants, size and color combinations will maximize their profits given the constraints on their production process. They can also tell you exactly what price to charge and which customers to target when. They can tell you which processes will meet their strategic goals: reduce costs, increase innovation, or add to the bottom line.
It is true that to achieve optimization, one needs to have a good toolset, good data and good skills. But that doesn’t mean you have to be a Fortune 100 company. Even small companies can use tools to optimize their processes with very little data, as long as the infrastructure is in place to measure response and calibrate your ongoing efforts.
You probably have a good idea of where you fall on the evolutionary spectrum. Of course, your goal is probably to move on to the next step. That’s really what this book is about. We’ll explore what it takes to develop your data-driven decision making to the next level. In this book, we will focus primarily on sales and marketing. In other books, we will cover topics such as operations, talent acquisition and retention, and research and development.
Evolution requires two things: infrastructure and support. Infrastructure comes in the form of knowing the data that needs to be collected and how to analyze it. The right people support the organization in ways that push it to do things a little differently than before.
It is important to note that in almost every survey of companies undergoing a process to become more data-driven organizations, Key driver Success is executive support. Otherwise, it is almost impossible. With this, as long as the infrastructure is in place, things fall into place.
As we go through the elements of a data-driven sales and marketing function, we can outline the infrastructure. We can also indicate where executive sponsorship can influence the process, but obtaining and keeping executive sponsorship is your responsibility. And it’s a serious one.
It is important to note that even if you have evolved beyond stage 0, it may be important to read the sections for earlier stages. who knows You might learn a thing or two that might help along the way. Each stage in between is intended to build upon itself, so it’s not entirely irrelevant to where you want to be.
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