Activating advocacy through analytics

In previous articles I considered how a business can maintain and even grow revenue levels through the analysis and application of data to drive targeted marketing, help pre-empt churn and develop relationship pathways. I suggested that this can also help determine who the next contingent of advocates might be and it’s this concept of advocacy that I wish to expand upon in this article.

Organisations have the ability to build and nurture advocacy tactics from within their own customer data and considering overall relationships with the organisation will assist in creating a relationship pathway that creates advocates. The object is moving the individual along the traditional ‘loyalty staircase’ from ‘Suspect’ (unqualified lead), to ‘Prospect’ (qualified opportunity), to ‘Customer’ (converted), to ‘Client’ (an engaged customer that has a low likelihood of churn), to the ultimate rank of ‘Advocate’.

But also recognising and pre-empting the potential churn points (when they may show themselves to be a ‘Rejecter’). Customers may become dormant, prospects can become turned off. There must be safety net to catch those that want to be saved and the strategy must make allowance for their rejoining perhaps at a different level.

From a database marketing or CRM point of view there are two types of advocacy – active advocacy and passive advocacy. Both can be managed through the database or a customer relationship management (CRM) system.

Active advocacy describes a scenario where a satisfied or engaged customer will recommend the organisation, its services or products to their peers and wider acquaintances. This will be by word of mouth, through member-get-member initiatives, peer-to-peer referrals, viral marketing or social networking. In this case the customer actively, consciously makes the referral.

Passive advocacy concerns using customer insight to facilitate the ‘cloning’ of new customers and the planning of pathways for existing ones. By analysing the behaviour, the profiles and history of customers to create segmentation, others who meet similar segmentation criteria can be deemed to appreciate similar status and services and so behave in a similar manner, thereby establishing the pathway that they should follow. The customers who are being used to create the templates for others are not intentionally advocating.

This analysis goes beyond reporting. One major difference between data analysis and reporting is that reporting provides a snapshot, a level of performance at a given point in time. It does not reveal why that level of performance or under-performance was attained.

This requires a train-of-thought process, an investigation to identify the reasons behind the results and it is the desire to find ways to improve performance and seize new business opportunities that calls for analysis rather than just reporting. It is this creative use of information and the knowledge of data that will lead to successfully exploitation of the organisation’s valuable data assets.

The challenge that faces organisations of all sizes is how to access and use reliable, high quality data to maximise the opportunity for acquiring information and create knowledge for commercial advantage. All of this can be achieved by collecting and maintaining the right data about customers, engaging them in a relationship and fusing the outcome of research or social media against the relationship database to enrich the segmentation and selection, thereby progressing them to advocacy.

The key is to understand how the customers wish to manage their relationship with the company.

I call it the ‘spectrum of engagement’ which runs from the apparently active customer who says ‘send me every communication, proposition and offer that there is’ at one end to the customer who appears distant or dormant at the other who says ‘I know where you are when I need something; don’t bother me’. The key concept to grasp here is that your most loyal member and potential advocate could exist at either extreme and, in fact, anywhere in between. On the one hand, the active and the other the aloof or one could say there are committed customers and those that participate on an ad hoc basis. You need to know where each constituent is on the scale and it is the organisation’s role to determine the position and use that knowledge to drive the relationship, the regularity and content of communications and the propositions offered.

So, how do you understand your customers? How can you identify ‘advocates’?

Active advocacy can be identified amongst committed customers, whose attitude and behaviour reflects satisfaction or who have been satisfied customers. Advocates will also hide amongst bloggers or complimentary contributors to the organisation’s chat-rooms and forums or social media whose engagement score or profile supports their sentiments.

Finally, the obvious ones will be those who have referred in the past or proven committed customers who have registered testimonials that can be used in marketing activity. These are the one whom the CRM should be inviting to participate at a higher level and should be included as the first wave for any viral marketing activity.

Identifying the passive advocates is where the analytical and data fusion techniques can be applied. Information from research surveys, from other external sources and from social media fused with the analytics from the data held on the organisation’s database to contribute the psychographic profile – their attitudes and aspirations and thus reveal the individual’s sentiment towards the organisation.

The fusion technique follows a fairly straightforward path. Firstly bring all the data into one database, making sure all those little departmental spreadsheets and odd copies of Access or Act are included! Then review the data and identify any requirement for enhancement or enrichment, perhaps adding some demographic or lifestyle information that may have an effect on behaviour or purchase decisions.

Next, mine the data and identify the segments or communities that go to make up the constituent base and define the differences in behaviour between them. This will facilitate what questions to pose in the research surveys or issues to seek from social media which I refer to as the ‘golden questions’; these will be the key differentiators between groups of customers.

Then execute the survey activity – often several variations of the survey are required to target the golden questions to right segments.

Research data can be both discrete and general. If research is discrete then it can be matched back to the subject, providing specific qualification criteria for those who respond. Information can then be extrapolated across other similar subjects to provide selection criteria, expanded profiles or segmentation.

If the research data is general, knowing which segment or community was subjected to the survey permits extrapolation in the same way.

The passive advocates will potentially be those with a positive outcome from the data fusion process. Their profile should be used to create a template for the relationship with other customers that look similar.

I envisage a three-element scenario at each step in the pathway: the state, the condition and then the action. In order for the step in the pathway to be identified a state must coincide with a condition; for example the constituent’s state may be that they are a new customer (say transacted for the first time within the last 3 months) and the condition is that they have made a subsequent transaction. The action may be a personal invitation from the MD to joint their loyalty programme.

Imagine it like a railway with the train being the customer. The track it is currently on is the state; the station it has arrived at is the condition; which way the points are set after the station directs the action, i.e. which track does the train take now.

These double triggers enable a greater granularity in the plotting of the customer on the pathway and the relevant action to be undertaken.

These three elements can then be interpreted as process or workflow and modeled in the CRM and by introducing the attitudes, aspirations and sentiments and extrapolating them across segments the organisation can refine one-to-one communications to move the constituent along the appropriate pathway.

The process can be extended by invoking a process of ‘cloning’ for new customer acquisition. Cloning, in this case, refers to the application of the appropriate relationship template to prospective members using the best members and most profitable customers to deliver high quality new customers – note: database marketers were practicing cloning long before we heard about ‘Dolly the Sheep’!

©Copyright Michael Collins 1998-2014. All rights reserved

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