Can we really access of all data we have on customers and make decisions on the fly?
Here, we look at the types of data which is available and describes how the single individual view is emerging as the way forward, not the ever dependant single customer view.
For many years, us marketers have been striving to achieve a utopian state by knowing everything there is to know about our customers. We have even labelled this objective as the “single customer view” but the main challenges organisations have found is:
• Managing the view on an on-going basis, given the demand for big data, or just about any new customer data, in the organisation is a continuous battle with a huge cost. (Data total cost of ownership)
• Keeping analytical data updated is unrealistic (data relevancy)
• Handling customer channel hopping is unachievable (data accuracy)
• Delivering recommendations on interactive channels end up latent or irrelevant (data appropriateness)
• Handling triggers do not have all the information to make the right offer or incentive (data timeliness)
• Service and Sales customer interactions are treated independently (data silos)
So with all these challenges in place can we truly have a single customer view? As we knew it, the simple answer is no! The single customer view is appropriate for on-going, organisational led communications, however for consumer led interactions, organisations will be paralysed to make a decision in real time due the constraints around the volume and recency of data.
In a recent IDC web conference, one of their key findings was: “the high demand for sales and marketing revenue analytics will pressure organizations into moving away from older CRM systems, which may cause infrastructure disruption.”
The single customer view, which marketers used to consolidate a vast amount of data to provide a single version of the truth, will always be needed, but will play a supporting not leading role. However consumers and the splinternet have changed everything. Consumers are no longer drones following brand orders and are transient in their use of channels but:
• The type of communications with customers is blurring (service, marketing, sales and research are simply a communication by the brand to the customer.)
• The channels consumers interact with will grow
• Consumer demand for instant decisions PLUS an assumption we have all the information they have told us at our fingertips will only continue
Organisations, which deliver this, will have a differentiated offering. If only they could act on this data to be more relevant and make it easier for customers.
Let’s look at two very simple, customer led, examples of the same scenario for a new customer, applying for a new credit card:
The customer was a previous customer who previously had a prepay card. They ended not using the card as they found it difficult to top up the card via a mobile device and ended lapsing as a customer.
1. Customer clicks-through on a banner, based on a 0% balance transfer offer
2. The customer re-navigates through the to the offer.
3. The customer opens the 0% balance transfer offers and complete the application form
4. Application form is received and customer approved
5. Customer goes back onto the website to find out about progress of the card, and has to re-navigate through to the right page and login.
6. Customer lost their login and rings the call centre, who do not know anything about the card application (as the application was made only an hour ago and their systems only update nightly.)
7. Customer is frustrated as they want to make sure they are in and able to sign for the card
8. Customer just waits and rings back. The call centre asks the customer who they are and what they are looking for, even after they have entered the information on in the IVR
9. The customer is then transferred to the new cards team, who ask the customer the same questions
10. The customer team ask for the customer’s address again to update their records as there was a mistake on the address
11. The update is made and the card delivery date is then provided
12. The customer has finally found out the answer.
Sound familiar? Surely it shouldn’t be this difficult?
How it could be then:
1. Customer clicks-through on a banner offering 0% balance transfer.
2. The customer is taken directly through to the offer page where they read through the offer.
3. The customer fills in the application form. A warning is displayed confirming his address, as there seems to be a mistake, given past customer records. His mobile and landline number are pre-populated based on previous customer data, and asked for confirmation
4. A SMS and email confirmation is sent to the customer with a link to a mobile app (derived from previous customer channel preference)
5. Customer downloads the app and logs in. A status update is provided on the first landing page before the app tutorial.
So is this truly achievable? There are a lot of interactions happening here and some fundamental decisions being made.
The short answer is yes, but before we discuss how, let’s look at the key ingredients, the types of data we have to work with.
Types of Data
Firstly we need to understand that there are two types of data which existing within an organisation. This is not the education of big data vs. personal data, public vs. private data, earned data vs. owned data, but a simplified look at data focused at the customer level. Just think about the data organisations collect on consumers and then think about the key data they need to make a decision.
This is the data, which never changes, or change very infrequently (less than once per week). For example a customer name, gender, address, email address, segment they are a member of, communications they have received or historic transactions.
This data provides a huge amount of insight into the consumer, to understand the current and future value, preferred channel (implicit and explicit), contact information and communications they have received in the past. This data gives us relevancy and eligibility.
However this data fundamentally cannot be kept up to date with the vast amount of data, which is being generated through the interactive and digital channels of today.
This is the data, which is changing all the time. The phone call and reason for call, the last click on the website, the referral from the search engine, the click on the advertising banner, the conversation at the till, a live chat conversation or a mobile app download and sign up or interaction.
This contextual information allows marketers to understand the reason for the interaction, real implicit activity, when a consumer is researching vs. ready to purchase, what to say when they are complaining, how to gain trust by understanding the customer. This data gives us appropriateness.
However this data cannot accurately drive customer behaviour in the direction, which is right for the organisation. This data has a limited picture, a snapshot in time if you like.
So what options do we have?
We need to be able to combine both data types. Firstly we need to understand the static data is not useless. In fact it is the most valuable information we have, however we also need to understand it is always not the most up to date picture we have of the customer. We can try and predict everything about a customer, but if they are making a complaint, we should . . . . . . Handle the complaint as best we can.
Creating your Single Individual View
So the steps we need to confirm is:
1. What data do we have (both static and dynamic)
2. What do I know and not know about my customers when they are interacting with us
3. What are the missing elements I need to make key customer recommendations (remembering it is not always about selling, it could be about capturing data for a longer term opportunity)
4. What channels can I realistically and practically interact with my customers through (for example payment or a complaint lines may not be the first priorities, but customer service, online payment or account management environments might be ideal
5. What technology do I have for:
a. Accessing the data
b. Analysing the data
c. Making decisions based on the data I have
d. Presenting the recommendation
e. Recording the outcome of the recommendation