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Here are the key questions to ask, and processes to follow, before technology implementation.
By JD Engelbrecht, MD: Everlytic
With many organisations increasingly turning to technology and data to unlock value, it is becoming more important to have a clear understanding of the liabilities and risks associated with data harvesting and data ownership.
The global news stream is awash with stories of data mismanagement and theft, as well as abuses of customer and employee privacy – making it critical for business decision-makers to guard against reputational and financial risks in relation to their data strategies.
As a starting point, let’s provide some context to this discussion and the dangers we’re tackling.
Arguably, the “big data” race started with everyone collecting as much data as they could. More often than not, Big Tech and consultants sold the dream.
The technology quickly became able to store and process obscene volumes of data at unprecedented velocity.
However, as discussed in the first part of this article series, we started with a technology solution to what is essentially a strategy and coordination problem (a very common pattern).
Indeed, we logged everything without asking ourselves what we wanted to do with it; whether we should be doing it; and what the consequences could be of ‘owning’ all of this data.
Now, there is such an obvious paradox that I love referring to. The scenario above can be likened to people saying that a house is an asset.
If it is a bonded house, it is a liability: you must pay off the principal and interest balances, and consider the net asset value should you sell the property.
Similarly, owning all this personal information and customer data is a liability – that is, until you find useful mechanisms to create value…and then you need to consider the net asset value, taking stock of the downsides.
These ‘downsides’ most often include:
- cost of compliance;
- potential risk of transgression of regulation or suffering a data breach;
- the expenses related to securing, storing, and processing data;
- the impact on the trust relationship with the customer related to data harvesting; and
- the potential PR consequences of overstepping.
Then, you need to offset your liability and risk with the potential benefits of running a data activation programme. These benefits encompass:
- growing market share;
- expanding share of wallet;
- enhancing the customer lifetime value;
- operational and capital allocation efficiency gains;
- de-risking your business;
- optimising price based on risk and value for customers;
- development of new products and services to diversify your offering; and
- enhanced customer satisfaction.
Offsetting key risks with the benefits
Logically, at the beginning of a data programme, you are sitting on a significant liability and you need to start offsetting the liability and risks with the upside.
The upside is significant, if you have a solid understanding, get your foundations in place, and then execute accurately and at pace.
As discussed in the previous installation, any data programme needs to be properly planned and structured to reduce the time to value, and also to allow you to service your risk at pace.
Most importantly, don’t incur the liability without having a clear route to value that exceeds the risk.
Now, don’t get me wrong: I am a strong proponent of creating rich and vast data ecosystems that harvest relevant data for the legitimate purpose to create value for the organisation and customers alike.
However, some organisations are overly risk-averse, which stifles a company’s ability to be competitive – and ultimately, useful to its consumers.
As a business leader, you must find the balance in order to unlock the right type of value with acceptable risk, whilst always being fair to the customer and complying with regulations.
Ultimately, our role as business leaders is to optimise shareholder value, consistently and intelligently.
With that in mind, let’s look at how you develop a clear route to value for your data strategy – having considered the risks outlined above.
The first step is knowing what you want to do with the data: this understanding will inform what data you should collect to keep things simple; to avoid wasting unnecessary time and resources; and to ensure you collect what you need to execute with impact.
Critically, don’t store data without the intent to use it …that’s always a risky idea.
These questions can guide you in the early stages of data collection: Do we know what we want? What do you want to say? To whom do you want to say it? What value does the conversation have? What data do you need to initiate and drive your conversation?
Without a doubt, you are going to be embarrassed and found wanting if you don’t know which sales conversations to have, and what drives each message in the conversation.
Ultimately you cannot deploy a marketing automation programme without this knowledge, even if your data and trust foundations are in place.
Start with the end in mind
We so often don’t take a breath to really consider what we want to achieve with our data and technology strategies.
So, to begin with, understand what you want: these are your goals. The next step is to determine the drivers of the ultimate behaviours: these are your objectives.
Then, design the conversations you want to have: these are your journeys.
From there, each conversation will have different messages to move the user to action. Each message should contain personalised content-based data and engagement.
Messages are set up on various channels such as email, SMS, automated voice, web push, app push, website promo cards, and soon, instant messaging.
Each channel has its context and it drives coherent frequency.
Importantly, all layers and every interaction should be driven by a data network with assistance by the automation platform.
Then there are transition flows between messages, journeys, objectives, and goals based on your data, and their interaction with your messages.