Datamine Guide To Creating Customer Insight · PDF fileDatamine Guide To Creating Customer Insight 2 While many organisations have expected that consistent data collection and closer

Datamine Guide To Creating Customer Insight 1

Datamine Guide To Creating Customer Insight

Datamine Limited

Auckland +64 9 303 2300

Sydney +61 2 8022 8332

Melbourne +61 3 83 999 450

0800 DATAMINE (0800 328 264)

www.datamine.com

15 Faraday Street, Parnell, Auckland 1052 © Datamine 2017 — All Rights Reserved


While many organisations have expected that consistent data collection and closer collaboration between market research departments and database marketing teams would have led to a stream of customer insights, most have found that one does not necessarily follow the other as a matter of course. This guide introduces what is meant by customer insight and lays out the building blocks for creating it. The skills required to create an effective insights team and some of the commonly used techniques for uncovering insight are introduced, along with suggestions and tips if using them.

Mike ParsonsManaging Director

Index: Datamine Guide To Creating Customer Insight

1. What is Customer Insight? It’s more than just ‘interesting’ Analysis and insight are not the same thing The skills you’ll need

2. Developing your Insight framework

3. Tools and techniques for Creating Insight Technical Creating a single customer view

4. Analysis Projects for Insight Customer knowledge discovery Customer segmentation analysis Customer acquisition analysis Customer journey mapping Media optimisation Customer churn analysis Loyalty programme evaluation

5. Summary

6. What now?

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It’s more than just ‘interesting’Analysis and insight are not the same thingThe skills you’ll need

1. What is Customer Insight?


What is Customer Insight?

Insight is more than just ‘interesting’Most businesses want to understand their customers: their needs, their behaviour, and the frustrations they face when dealing with an organisation. Customer insight comes from compiling and combining data, from multiple sources, to paint a picture (see fig 1.) But for analysis of this data to actually be considered insightful it has to be useful in some way. Certainly, analysis that reveals something interesting about a customer might tickle the brain, but what value is it really adding? True customer insight must go beyond showing something interesting, to showing how a business can actually use that information to better connect its brand with its customers. Insight should help drive the company’s organisational thinking and decision making. It’s about having the whole picture, visible to the whole organisation from the executive suite right down to customer services staff.

From a reasonably simple statement — the desire to understand customers and gain insight — unfolds the complexity of delivering it. The creation of customer insight should not be left to a small, tactical initiative undertaken by a team working in isolation from the business. Instead, it requires an alignment between multiple departments of a business — IT, marketing, analysis, product, operations and customer services — across its organisational goals and investment in people, technology, systems and processes.

Analysis and insight are not the same thingWhile the words are often used interchangeably; analysis and insight aren’t the same thing. To illustrate the difference between them, consider a direct marketing campaign running two offers:

• At a basic level, the response rate for each offer can be measured. Offer A resulted in a 6% response and offer B in 8%

Market Intelligence

Customer Analytics

Sales & ServiceStaff Feedback

Market Research

Customer Complaints & Enquiries

CompetitiveInformation

CustomerInsight

Fig 1: Creating customer insight requires alignment across an organisation


• In the champion/challenger approach, offer B would become the champion as it had a higher response rate (assuming everything else was equal). Analysis would suggest, therefore, that any new offer would be tested against offer B

• However, if we take the analysis a stage or two further, a customer profile of the responders might show that very different ‘types’ of customers responded to offers A and B — and that both these groups had different characteristics to non-responders

• Therefore, a greater response rate for both offers A and B could be achieved in

a future campaign if the offers were more tightly targeted towards customers possessing the characteristics of the original responders to offers A and B

• The ‘insight’ comes from modelling what the ROI would have been had the knowledge gained been applied to the original campaign. That is, applying what you know now and arming marketing with the facts to confidently continue with offers A and B — and perhaps identify another appropriate offer C for non-responders

The skills you’ll needThe use of the word ‘data’ can see many C-level executives turn to their IT team for answers, but it’s likely that the expertise required won’t be found there. Consider, for example, the skill-set required by the Customer Insight Manager (often referred to as the Customer Experience Manager). Above all else, they will need to see the big picture, to be collaborative by nature (with internal departments & external business partners), and possess the drive to seek out the data that will enable the business to make evidence-based decisions regarding its customers.

They will also need an insights team (not necessarily all in-house) proficient in the following disciplines: database design, data modelling, data analysis, campaign analysis, systems support, market research, database marketing and CRM — business to technical and back again. As creating insight is often more art than science, the data analysts and modelers in particular need to be curious, pragmatic and capable of lateral thinking with a ‘can do’ attitude.


2. Developing your insight framework


Assuming you have a customer insights team in place, answering the following questions — first about your organisation, and then about your customers, will provide a simple framework for creating insight.

Fig 2a & 2b illustrates many of the questions, which, when answered, will provide customer specific insight.

Insight

Are customercomplaintsresolved?

What wouldthey like to buy?

Who are ourcustomers?

How is theeconomy

faring?

What media dothey consume?

What service levels do

customersexperience?

What docustomers buy?

Where dothey live?

How docustomers behave?

What do theythink and feel?

Do we meetcustomer

expectations?

InsightWhat do we

already know about our

customers?

How will we prioritise

what’s needed?

What data do we need?

How will we generate and communicate

insights?

What’s our strategy to achieve our

goal?

What data do we have?

Where are the knowledge

gaps?

What’s the goal of this

organisation?

Fig 2a

Fig 2b


3. Tools for creating insightTechnical

Creating a single customer view


Technical From a technical perspective, there are a number of tools necessary for creating customer insight

• Databases: The database will typically be your internal legacy system. This could be anything from Excel and Access through to MySQL, Maria DB and MS SQL Server

• Statistical analysis applications: These solutions manage the advanced analysis required for multivariate and predictive analytics. Commonly used packages include IBM’s SPSS, R (from the R Project for Statistical Computing) and SAS

• Visualisation solutions: Typically these tools query relational databases and spreadsheets, and can generate a range of different graph types and mapping visualisations. Popular applications include Tableau, Microsoft Power BI, IBM Cognos, and MapInfo from Pitney Bowes

For these tools to be with applied with any efficacy, however, there is a simple data prerequisite — a single customer view. Visualisation tools like

Tableau make complex data easier to understand


Creating a single customer viewThe key to all customer insight is in an organisation’s data, and critical to unlocking that data is the single customer view. Explained simply, a single customer view includes all the relevant data your organisation currently possesses regarding that customer. This could include contact & demographic details, products purchased, preferred communication channels, previous responses to marketing activity and more.

The benefit of assembling this data into a ‘single view’ is that it empowers marketers to better target and personalise future customer interactions.

The issue, however, is that this information is oftentimes held in a number of disparate databases and a single customer view is not immediately available. This can be a significant obstacle for companies wanting to gain customer insight, however, an effective solution is possible in the form of a ‘datamart’. Here the important data from disparate systems and data silos is drawn into a separate data depository. The data is transformed — cleaned, matched and de-duped — and a single customer view is created. Updates made to client records in the individual systems are then automatically populated into the datamart view, so the single customer view is kept current.


Customer knowledge discovery • Take a staged approach • Stage 1: Basic analysis • Stage 2: Profile specific groups• Stage 3: In-depth patterns and trends• Outputs from a knowledge discovery • Insight visualisation

Customer segmentation analysis• Ways to segment • Follow a phased approach • Phase 1: Plan• Phase 2: Develop• Phase 3: Implement• Phase 4: Measure & Review

4. Analytics ProjectsCustomer acquisition analysisMedia optimisationCustomer journey mappingCustomer churn analysis • Step 1: Quantifying the characteristics of churn• Step 2: Understanding the key factors surrounding churn• Step 3: Developing the predictive model• Step 4: Implementing retention initiativesLoyalty programme evaluation


There are a host of different analytics projects that can be applied to the data to create customer insight. Of course you don’t have to deploy them all at once, instead, be driven by the agreed business priorities. Some common analytics projects for starting to create insight include the following:

Customer knowledge discoveryIn terms of creating an agenda of customer insight ‘must dos’, knowledge discovery should be at the top of your list. The overall goal of the knowledge discovery process in the context of customer insight is to extract knowledge from a data set and present it in an understandable way. Importantly, you need to:

• determine what you need to understand• identify all the relevant data sets • determine meaningful ways to compare customers and time periods

There are normally several stages in the knowledge discovery process — and decisions to be made regarding how far the analysis is taken (see fig 3). The starting point is the previously discussed single customer view which provides the base for the analytics, along with other information sources such as market research. The focus needs to be on new areas of interest, so do not make assumptions (you will be surprised how often they are wrong) and let your data tell its story. Work iteratively when discovering insights, as uncovering an area of interest may change the direction of the analysis.

Take a staged approach The following approach is recommended, with review sessions undertaken between the technical analysts and the business users at the end of each stage (as a minimum — more interaction usually leads to improved outcomes).

Basic analysis of the customer

base

Profiling specificgroups

In depthpatterns &

trends

Fig 3: The stages of knowledge discovery


Stage 1: Basic analysis

The following are some potential areas of interest for the basic analysis of the customer database, though these will vary from industry to industry and company to company. • Number of unique customers• Number of new customers acquired over

the last n years • Number of customers lost/churned over

the last n years • Geographic location of customers• Number of your products held and/or

combinations of products held• Basic demographic profile

Stage 2: Profile specific groups

Next, investigate further by profiling specific customer groups. Typical groups may include new, active, inactive, lost and high value. It is often useful to repeat the basic analysis process on each specific group of customers. Additional factors to review are frequency and value, customer profitability and channel usage.

Stage 3: In-depth patterns & trends

An important aspect of knowledge discovery is to look at your customers in terms of changes over time to establish patterns and trends. After basic analysis and specific group profiling are completed, undertake more in-depth analytics on groups of interest. This may include:• The time between product up-take or

transactions for active customers• Behaviour patterns prior to leaving for lost

customers• Activation levels and a profile of active

customers versus inactive• Seasonality patterns, who buys what and

when?

Outputs from a knowledge discovery The final output from the knowledge discovery will be a comprehensive report on your customer base. This will present information at both a point-in-time and over time. It is useful to have this information as a benchmark to compare against, for example, is the number of customers more or less than last year or are retention rates falling?

Insight visualisation The ability to gain customer insight from a knowledge discovery is aided greatly when the data is presented graphically. When data sets are large or abstract, visualisation helps make them easier to understand — especially for non-analyst users. See section above for common visualisation tools.


Customer segmentation analysisCustomer segmentation is the practice of dividing a customer base into groups of individuals that are similar in specific ways, such as age, gender, geography, socio-economic, interests, spending habits and so on. Applying segmentation allows companies a better insight into their customer types and permits the allocation of marketing resources to best effect. In short, good segmentation leads to better marketing as it enables businesses to accurately predict and fulfil the needs of each customer segment.

Ways to segment While there’s no ‘right’ way to segment, a good rule of thumb is to develop the fewest number of segments that make sense from a marketing perspective. Key considerations when answering the ‘how to segment’ question are; what are the company goals that are driving the desire for a customer segmentation, who will be impacted by it, and have you made a realistic appraisal of the data available upon which to base the segmentation? Common approaches are• single or multiple factor demographic segmentations e.g. age or age and income• geographic or geo-demographic• customer value• behavioural• life-stage • attitudinal • a combination of above

Follow a phased approach There are four distinct phases involved.

Plan Develop Implement Measure Review

Fig 4: Phases of segmentation development


Phase 1: Plan

The planning workshop is a key phase in segmentation development. First, make it clear what you are trying to achieve, then discuss data sources, limitations and whether any research is required. Useful questions include:• How many segments make sense from a marketing perspective?• How is the segmentation going to be used? Acquisition? Retention? Cross-sell and up-sell?

Phase 2: Development

The development process involves; getting a handle on the data, considering the different approaches, looking for relationships in the data, reviewing the options, and choosing the best option for your business. Perform both univariate (looking at one variable at a time) and multivariate (the simultaneous observation and analysis of more than one variable) analysis.

The purpose of multivariate analysis is to understand the relationships between variables and their relevance to the segmentation. For example; how many of your customers fall into specific age categories (univariate) and is there a relationship between the gender and age of your customers (multivariate). True insight is often revealed in these previously unknown relationships.

Phase 3: Implementation

Consider how frequently the segmentation needs to be run – more frequently for fast-changing environments (monthly), less often for low transaction environments such as insurance. Think about new customers and how they are to be handled in the segmentation e.g., put into a newbie segment for the first three months?

Phase 4: Measure and Review

Segmentation is not a one-off project, your customers and market environment change and the segmentation needs to change to reflect this. Measure and report the key metrics for each segment and track and report on the movement between segments. It can be tempting to implement segmentation and then leave it, but its true value comes from the measurement of results and review of the processes to enhance marketing efforts and ROI over time.


Customer acquisition analysisCustomer acquisition is the phrase that encompasses all the initiatives an organisation may undertake to attract new customers to the business. This includes above the line activity like television, radio and print advertising, and through/below the line initiatives like direct mail, email marketing and public relations. All these activities come with associated costs.

In very basic terms the cost of customer acquisition is calculated by dividing all the money spent on marketing and promotions during a specific time period, by the number of new customers acquired. For example, if the marketing and promotional spend for a company over a 12 month period was $400,000, and 25,000 new customers were acquired, then the overall acquisition cost per customer for the year would be $16.00

Drilling down for additional customer acquisition insights, however, can empower more efficient use of media. For example, if $10,000 was spent on email marketing campaigns over the same 12 month period, and 1400 new customers were identified as being secured via email campaigns, then the per customer cost of acquisition using email for the year would be $7.14 per customer — less than half the overall cost. Insights like this into the cost of customer acquisition should be applied to increase the return on investment from marketing campaigns.

Media optimisationMeasuring media performance provides valuable evidence for business cases, demonstrating ROI and monitoring performance. The return on investment is easier to measure directly for some activity (email marketing for example) as opposed to more traditional promotional methods like print or radio ads. As media continues to fragment, it is becoming increasingly difficult to reach the right audiences and to ensure that advertising spend is allocated to the most appropriate channels. Once you gain insight into the purchasing patterns of your customers, however, you can place your emphasis on only those activities that influence customer spend — isolating what stimulates the behaviour your business requires.


Customer journey mappingA customer journey map documents your customer’s engagement with your company from their first interaction via one of your touchpoints (websites, call centres, email campaigns etc.), through to a purchase and their ongoing engagement. The journey from researching your product or service, to purchasing, may not necessarily be a linear one and will differ from customer to customer. Nonetheless, it should be possible, using data analysis, to create a number of typical customer journey maps for different customer segments.

Journey maps can be represented graphically and show the step-by-step experience with your company from the customer’s point of view. For marketers, a customer journey map identifies the key interactions between a company and its customers and assists in identifying where the journey could be simplified or enhanced. Stages of the journey where customers may be becoming stuck/confused and are leaving the sales funnel can also be highlighted and problems remedied.

1. A customer conducts a Google search looking for the solution to a problem they face

2. Customer visits your website

3. The prospect opts in to follow your company on social media — Linkedin, Facebook or Twitter for example

4. Customer returns to your website and downloads a brochure or some other collateral specific to their issue

5. Customer purchases from your website

?

Customer churn analysisThe accepted rule of thumb is that it costs five times as much to acquire a new customer as it does to retain an existing one. Given that, attention paid to reducing customer churn should be an integral component of any customer insight strategy. Churn analysis is the first essential step towards implementing effective customer retention and loyalty programs. More specifically, it will: • Establish the cost of customer churn to your business and provide justification for appropriate investment in customer retention and loyalty initiatives• Target retention efforts on high-value customers with a high risk of churn

Fig 5: A potential customer journey map


There are a number of distinct steps leading to effective churn management and the biggest challenge can be breaking what may seem a mammoth task into manageable pieces.

Quantify Understand Model Implement Measure &Refine

Step 1: Quantifying the characteristics of churn

The first step in the process is to define what churn means to your organisation as this will help identify the customer segments you need to proactively manage. Often, both voluntary and involuntary churn exist. An example of involuntary churn is a bank closing a customer’s credit card as a result of non-payment. Usually, for churn management, the focus is on voluntary customer churn. The next consideration is whether to focus on both hard and soft churn. Hard churn is when there is a defined event that signifies churn, for example, a customer closing their account. However, this approach may be too simplistic, and soft churn could be a more appropriate measure. A customer may be defined as having soft churned if they have not transacted with your organisation for a period. The length of time varies depending on the nature of the industry and often on the customer’s initial behaviour (namely, transactional frequency). For example, a shorter period would be used if focusing on supermarket shopping (as we shop and eat every week) compared to booking a holiday.

Step 2: Understanding the key factors surrounding churn

A common method to identify likely churners is to compare the profiles of customers who have previously churned with customers who have not. This will involve a range of data, including service history, behavioural, demographics, channel, value, usage etc. Integrating any specific information collected at the point of disconnecting (reason codes for example) is also useful. This behavioural profile provides insight into typical customer behaviour patterns prior to churning and will frequently identify a ‘moment of truth’ in the customer journey where churn becomes statistically probable.

Fig 6: Steps towards analytically driven customer retention


Step 3: Developing the predictive model

Based on this analysis a predictive customer churn model can be built and tested. Applying the model to an historic data set where the status of the customer is known, will enable a table showing the proportion of false positives and false negatives to be produced. The proportion of these should then be compared to the potential profitability of these customers and the cost of a churn prevention initiative. The objective is to optimise the number of accurate predictions and the resulting ROI from preventative actions.

Step 4: Implementing retention initiatives

Key findings and recommendations based on the analysis provide the foundation for implementing customer retention initiatives to minimise the future impact of churn. Examples of the type of insight you can expect include the best time to contact a customer based on a ‘moment of truth’ and a defined demographic profile to help with creative executions and specific offers. Finally — measure the effectiveness of these initiatives and refine them as needed.

Loyalty programme evaluationLoyalty has become a buzz-word in many organisations wanting to get closer to their customers and more than half of New Zealand’s consumer-based businesses are now offering some form of loyalty programme. Loyalty programmes are structured marketing efforts that should reward and encourage loyal buying behaviour. The issue for many schemes, however, is that they are providing rewards at a cost, for customer behaviour that would have occurred anyway. To address this, organisations are looking to increased customer insights as a means of tailoring loyalty campaigns to drive additional purchases. To that end, data analysis can:• Provide tools to quantify the financial impact of adjusting a programme• Model changes to a loyalty programme on customer behaviour (and therefore revenues) • Measure the commitment of members of loyalty programmes• Analyse the real impact of loyalty campaigns based on recorded customer behaviour• Reduce the reliance on expensive market research by using customer segment behavioural analysis


5. Summary


For any organisation, no matter its size or its industry, the fundamental building blocks of customer insight are:• Clearly defined company aims and objectives• A commitment to customer and data-driven

marketing• Organised and accessible data delivered via a

single customer view• The right skills in the insight team• The appropriate technology for the job • The application of analysis processes that align

with company objectives

As the preceding guide has shown, gaining customer insight is not about employing an analyst, or even a team of analysts. Instead, true insight will result from a clear strategy, an organisational commitment to the analysis process, collaboration and input from many different departments, and an alignment between what you are looking for and the goals of the business.


6. What now?


What now?

Need some help creating Customer Insights? At Datamine we’ve had over 20 years of experience in bringing all this together. We’re technology agnostic and we’ll work collaboratively with you to deliver the right solution for your business — whether you’re starting at zero or you’re a way down the track already.

Contact us today

Datamine Limited

Auckland +64 9 303 2300

Sydney +61 2 8022 8332

Melbourne +61 3 83 999 450

0800 DATAMINE (0800 328 264)

[email protected]

www.datamine.com

15 Faraday Street, Parnell, Auckland 1052


© Datamine 2017 — All Rights Reserved

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Datamine Guide To Creating Customer Insight · PDF fileDatamine Guide To Creating Customer Insight 2 While many organisations have expected that consistent data collection and closer