Commercial Analytics is the kind that makes money. From data to dollars, insights to income, this is all about how to run the business better. To do it and to do it well you need certain capabilities in place. This article builds a map of those business capabilities to help you assess, understand, and plan your business.
Usually we talk about this and we are happy to talk to you about it (just contact us) but we recently had occasion to make a slide pack that covered some of the materials as a stand-alone presentation. This article is based on that pack which is also available as a PDF:
Download CYBAEA Commercial Analytics Capabilities presentation (PDF).
Commercial Analytics is the kind that makes money. From data to dollars, insights to income, this is all about how to run the business better.
It is about identifying opportunities and acting on them.
It is about creating new opportunities and new demand.
It is sales, marketing, retention, product development, operations, risk, and reward.
It is the present and the future.
This diagram shows you what the commercial analytics landscape looks
like and where it is developing. We will develop it step by step in
Let us build it up one step at a time.
Commercial analytics starts with the execution because that is how we make money. In a marketing setting this means that you have a well-functioning campaign execution capability.
It is scientific marketing because it enables very fast test and measurement of campaigns. There should be no barriers to try out ideas and quantify them. You need this capability because you simply do not have all the answers and all the knowledge.
Typically, you should be able to get a campaign from idea to measured results (campaign velocity) in no more than two weeks for your standard channels.
Once you have tested and possibly refined your ideas into a winning, money-making campaign, then you execute it not just once but typically again and again. It may be the daily welcome programme, the cross-sell for the weekend activities, the fortnightly proactive churn activity, the monthly loyalty program, and the annual renewal. Whatever is right for your industry and business.
And the campaigns are not just triggered on time but triggered on events in the customer’s life, such as new purchases, changed usage, unusual behaviour, etc.
They are of course multi-channel and multi-stage campaigns where and email may be followed by a letter and then a call if the customer is of high enough value.
We do not forget that the best time to talk to a customer is usually when he or she contacts you. To do this effectively we need dialogue management or real-time inbound marketing, which is the decision support for our call centres, retail stores, and web sites that enables us to keep the conversation and the offers relevant and timely. Typically, for inbound marketing in a call centre you would present new offers to the customer 20-50% of the calls and expect a conversion rate of 25-50% of the presented.
Product, pricing, and proposition
Outside traditional direct marketing we do not forget product, pricing, proposition and go-to-market. We consider them to be execution channels for analytical insights. And like direct marketing we will be able to quantify the effect of our actions (though often with less rigour than then control group validated activities).
Insights should revels gaps in our product offerings as areas
of our customers’ lives that we are not servicing (or not servicing
well). Pricing strategies and bundles are another well-known
outlet for insights, and includes market research and product proposition and positioning development.
The single customer view or the 360° customer view. It includes:
Customer attributes, which is your rarely-changing information like name and address and also socio-demographic information that you may have purchased. For example:
Name, address, postcode, email,…
Age, marital status, children, …
Payment methods and -details (credit cards, bank accounts, …)
Relations to other customers (family, business, …)
Contact preferences (marketing permissions)
Demographics (NRS social grade A/B/C1/C2/D/E, ONS socio-economic
class 1-8, Mosaic UK Group/Type, …) – purchased or own
Transactions are the customer’s purchases, payments (including late and missed payments), and other information about how he transacts with your business. Examples:
Purchases (store, mail-order, online, …)
Bill payments (including late payment information)
Current and expired subscriptions (or other products / promotions)
Usage data (calls on mobile network, programmes watched on TV subscription, bytes transferred through ISP, …) if appropriate for the business
Interactions are when the business and the customer interact and are
important to understand cost/profit (many calls to the call centre are
expensive), as churn indicators (many visits to the “how to
leave” section on the web site) and much more. Examples:
Calls to the call centre, including call reasons and notes
Visits to the web site, including what type of information did the
customer look at (for example: adverts for new products or technical
help for existing product)
Conversations in store
Conversations with field agents (e.g. service or installation)
Indirect channel interactions (agents, distributers, field, etc.)
Attitudes: How does the customer feel? Traditionally through focus group samples but now increasingly using text mining and social network techniques.
Focus groups and market research results
Emails to the company (suggestions, support, sales, etc.)
Call centre notes are a rich source of customer sentiment
attributable to individual customers
Comments on company forums, technical support sites etc.
Facebook, Twitter, blogs, and other social media outlets
Some of these are attributable to individual customers while others
provides samples or aggregate information. All is valuable.
Campaign data is information about which offers you have extended to each customer and how they responded.
Campaign information includes id, date, time, type of
campaign, structure (e.g. multi-stage), etc.
Offer information includes information about the product, the
promotion (e.g. 20% off), and the presentation (e.g. graphics, call
centre script, ad copy, …), the channel, the agent, etc.
Outcome information includes click-through, shopping cart,
purchase, usage, and any other relevant activity from the customer
(e.g. ‘like’ the product on Facebook)
Information will be available at different levels, depending on the source and the business context
B2C organizations will often have some information at the household level and some at the level of the individual.
B2B companies will often have a user – chooser type structure where one person or group (the chooser) may be responsible for buying for many users. (Think mobile phones, furniture, or computers.) And there will be complex company structures which may be important
Indirect businesses will have a further level in the hierarchy to include the distribution channel (e.g. agent or reseller)
Customer Analytical Record (CAR)
To simplify, many organizations introduce the Customer Analytical Record (CAR): a standard set of data, including aggregate data, stored as attributes against each customer over time. Typically there would be at least 24 months data and from a few thousand to ten thousand attributes. We aim that at least 80% of our analysis and reports can be done on this data set. Data may include
Standard customer attributes
Aggregated usage (last month, 3- months, 12 months)
Aggregated billing / purchases
Aggregated interactions per channel
Attitudes summary (e.g. satisfaction scores and trends)
Benefits of the Customer Analytical Record include:
Standard data set means consistent data definitions (single truth)
and a gentler learning curve making new staff more productive
Simpler format directly suited for many models and analysis
(e.g. churn can be treated as a regression / classification problem
rather than a time-series / hazard problem) reduces complexity and
improves the time to execution.
The CAR enables us to have a Model Factory where the predictive models supporting our many ongoing customer activities and campaigns (see Execution above) are automatically refreshed and re-validated at regular intervals.
Markets and customers change, and so the behaviours of customers change. We want to avoid stale models that predict yesterday’s reality but does not deliver results today. We need to refresh the models to take into account current customer behaviour and we want to re-validate the models to make sure they are still predictive and stop any campaigns that are based on models with no (or negative) lift to allow us to re-analyse the situation.
Let’s talk about people. In our model, Analysts work primarily on the data in the CAR to produce dashboards, reports, and models. Models that are part of ongoing campaigns are included in the Model Factory for automatic updates.
Some analysts will have the ability to gather data from other systems, be they the main data warehouses or the operational source systems. We call them Data Wranglers and they are usually analysts with a longer tenure.
To complement Analysts and Data Wranglers you may have Team Leaders or other management function to handle scheduling, workload, quality assurance, and stakeholder management.
We sometimes borrow a technique from agile software development and use pair analysis where two people work together on the same workstation at the same time to develop the insights. One person, who is an analyst, types in the code or drives the analysis workbench. He or she is called the driver. The other person reviews the work and comes up with ideas. This person is called the observer and may be an analyst but we have found it extremely useful to include business people such as product or channel managers in this role, depending on the business problem.
It takes a little experience to know when to apply the pair analysis
technique, but it can be extraordinarily rewarding.
Commercial, Customers, and Data
The single most important component for success of commercial analytics is this: to have people who all understand data, customers, and commercials.
Data is where the recruitment focus often lies: can this person manipulate data and create models?
Customers are crucial: the actions are actions for and with customers, actions to change customer behaviour. Team members need to be able to get into the head of the customer.
Commercial results is what it is all about and you can have great models and great marketing, but if the changed behaviour is not profitable or cannibalises other profits then there is really no point.
You probably will find it hard to recruit for this combination. It is relatively easy to recruit for the technical data skills. You can look at the candidate’s history to see how often it mentions money and customer behaviour; this will give you an indication. Mostly you have to grow this talent internally, and many of the techniques we have already described are designed to help you with this:
Start with the execution; this will help focus on changeable
behaviours and commercials results. Pair analysis with
observers from the business helps transfer commercial skills and
customer knowledge. The CAR keeps you focused on the results,
not the technical data wrangling.
The Analytical Shift
Times are changing. So far we have discussed traditional analytics which uses relatively static data (e.g. from a data warehouse), perhaps supplemented with key pieces of interaction data for real-time inbound marketing.
In this traditional world we tend to look for the norms: the trends, the clusters (e.g. segments), the networks, and so on.
Increasingly we are interested in exceptions: the deviations from trends, the outliers, and the network singletons or orphans.
This was always the case in the industrial setting where we were interested in machines that were about to fail or spotting defects.
The same approach applies to marketing and other commercial analytics. It is when I step out from the crowd that I reveal who I really am as a person. But this kind of processing requires different skills, tools, and techniques.
It uses bigger data because we are now analysing raw events, not just summaries. It is dynamic and real-time, and the results are expected in real-time. When I step out from the crowd is also often the right time to communicate.
And it is event stream processing which is very different from
database processing: you now have to worry about sequence neutrality
(same results if the events arrive in different order) and much more.
How we can help
Information: Web site, newsletter, and custom reports
There is a wealth of resources and information available on our
web site (www.cybaea.net): please use it and sign up for our newsletters.
We understand this landscape and how it is evolving. We have
been through the changes before.
We provide custom research and reports using our industry contacts
Change: Rapid business benefits
We can help you understand what you really need and what is optional.
We can put money against the opportunities to develop a vision
and business case for where you need to be.
We can help you develop a change programme that is funded from
the benefits it delivers along the way.
Talent: Interim management
We can step in and provide talent you need to achieve success
right now, and train or recruit for permanent operations.
We only provide experienced people who have done this before and
who deeply understand commercials, customers, and data. They are
all credible at every level from the board to the technical
Service: Commercial Analytics as a Service
For certain industries we can host the analytics and provide you
with the norms, exceptions, and marketing actions.
We work with a number of vendors of hosted software to provide
the analytics capability as a service. For some organizations we
provide a custom solution.
We can provide the reports, dashboards, and insights or we can
directly provide the marketing actions
Please let us know your thoughts in the comments or by contacting us.
Don’t forget that this material is also available as a stand-alone presentation:
Commercial Analytics Capabilities presentation (PDF).