Figure 8.1 Gartner’s Analytics Ascendancy Model.
Descriptive analytics
As you consider your pain points and work to raise KPIs and to reach your strategic goals, many of the issues that surface will be as simple as ‘We don’t know X about our customers’ (behaviour, demographics, lifetime value, etc.) or
‘We don’t know X about ourselves’ (costs, inventory movements, marketing effectiveness, etc.). Getting data-driven answers to such factual questions is what we call ‘descriptive analytics’. It is the process of collecting, cleaning and
presenting data to get immediate insights.
You’ll look to descriptive analytics for three applications:
Operational requirements. Each of your departments will need data to operate effectively. Your finance team, in particular, cannot function without regular, accurate, and consolidated figures. This is why companies often place their business intelligence (BI) teams within the finance division.
Data insights. Put as much data as possible into the hands of decision
makers. You may choose to have your data (BI) teams situated close to your strategy team and have analysts embedded within your various business units (more on this in Chapter 10).
Damage control. If you’ve been operating without visibility into your data, you may be blindsided by a drop in one or more KPIs. You need to quickly determine what happened and do damage control. The less data-driven your company, the more likely it is that the crisis will be directly related to profit or revenue (else you would have detected a change in lead indicators). You’ll first need to make up ground in descriptive analytics, then move quickly to diagnostic analytics.
To do descriptive analytics well, you’ll need the following:
Specially designed databases for archiving and analysing the data. These databases are most generally referred to as data warehouses, although you may use similar technologies with other names.
A tool for constructing and delivering regular reports and dashboards. Very small companies start with a tool such as MS Excel, but most should be using a dedicated BI tool.
A system that allows your business users to do self-service analytics. They should be able to access data tables and create their own pivot tables and charts. This greatly accelerates the process of data discovery within your organization. To implement a self-service system, you’ll need to establish additional data access and governance policies (see Chapter 11).
Diagnostic analytics
Diagnostic analytics are the problem-solving efforts, typically ad hoc, that bring significant value and often require only minimal technical skills (typically some standard query language (SQL) and basic statistics). The diagnostic effort consists largely of bringing together potentially relevant source data and teasing out insights either by creating graphs that visually illuminate non-obvious trends or through feature engineering (creating new data fields from your existing data, such as calculating ‘time since last purchase’ using your customer sales records).
A well-designed graph can be surprisingly useful in providing immediate
insights. Consider the graphs presented in Figure 8.2 from Stephen Few’s book.60 Start by looking at the simple data table below to see if any insights or trends stand out.
Now consider two different charts constructed from these same eight data points:
Figure 8.2 Employee job satisfaction.60
Notice that the second graph immediately provides a visual insight which does not stand out from the bar chart or the data table: that job satisfaction decreases with age in only one category.
Visuals are powerful tools for diagnostic analytics, and they demonstrate how analytics can be both an art and a science. You’ll need creativity and
visualization skills to discover and communicate insights through well-designed tables and graphs. As we’ll discuss more in Chapter 10, it’s important to have someone on your analytics team who is trained in designing graphs and
dashboards that can bring your analysis to life.
Predictive analytics
Predictive analytics help you understand the likelihood of future events, such as providing revenue forecasts or the likelihood of credit default.
The border between diagnostic analytics and predictive analytics is somewhat vague, and it is here that we see techniques that require more advanced analytics.
Consider customer segmentation, in which an organization divides its customer base into a relatively small number of segments (personas), allowing them to customize marketing and products.
Your initial segments probably used customer demographics (age, gender, location, income bracket, etc.), but you should also incorporate more refined data, such as expressed preferences and habits. RFM (Recency, Frequency, Monetary) is an example of a traditional customer segmentation approach based on purchase history, but the data that informs today’s segmentations should include big data: online journey, and sometimes audio and visual data. To form these advanced segments, you’ll bring in a lot of data and should use specialized statistical and algorithmic skills (such as principal component analysis, support vector machines, clustering methods, neural networks, etc.)
You’ll be able to do much more powerful analysis in predictive analytics. You may be predicting events several years in the future, such as credit default;
several weeks or months in the future, when forecasting revenue, supply or
demand; several days in the future, when forecasting product returns or hardware failures; or even just a moment in the future, when predicting cart abandonment or the likely response to a real-time advertisement.
The abilities to accurately forecast supply and demand, credit default, system failure or customer response can each have significant impact on your top and bottom lines. They can greatly increase customer satisfaction by enabling
adequate inventory levels, relevant advertising, minimal system failures and low likelihood of product return.
Prescriptive analytics
Prescriptive analytics tell you what should be done. Think here of optimal pricing, product recommendations, minimizing churn (including cart
abandonment), fraud detection, minimizing operational costs (travel time, personnel scheduling, material waste, component degradation), inventory management, real-time bid optimization, etc.
You’ll often use these four analytic layers in succession to reach a goal. For example:
1. descriptive analytics would flag a revenue shortfall;
2. diagnostic analytics might reveal that it was caused by a shortage of key inventory;
3. predictive analytics could forecast future supply and demand; and
4. prescriptive analytics could optimize pricing, based on the balance of supply and demand, as well as on the price elasticity of the customer base.
Keep in mind
You can realize tremendous value simply by organizing your data and making it available across the organization. More advanced projects can come later.