On our recent webinar that explored AI and ABM, one area we explored was how AI can be extremely useful when it comes to analysing data at scale.
AI, currently, is largely subject to the same restrictions as most computers – only as good as the data that is entered into it. According to Gartner, poor data costs B2B Tech Companies around $12m on average, and with AI experiencing exponential growth this poor data could cost companies more in terms of opportunity cost and loss of market share.
Following multiple projects where we’ve analysed large amounts of demand generation data in order to support the ABM strategy process, here’s our short guide to the three main areas you’ll want to prioritise in order to get your data where it needs to be.
Don’t do it for the sake of it, and know what you want upfront
Once organisations realise the power of AI, particularly when it comes to analysis, it’s tempting to start analysing everything and pretty soon, rather than drowning in data, you’re experiencing paralysis by analysis.
Focus firstly on understanding what you want from the data, and why. If you need the Ideal Customer Profile (ICP) process to be more objective due to internally differing opinions, then analysing customer sentiment will be your objective. Similarly, perhaps you need to understand which sectors and job functions are responding to which channels and content types in order to define an effective content strategy – whereupon campaign analysis is what you need.
Another use case could be hyper personalisation, this is extremely useful when it comes to analysing existing customer data. Combining data sets across your organisation across every stage of the customer journey, including post sale and finance, opens up significant opportunities for personalisation that can unlock growth in your top 20% of companies.
Don’t forget unstructured data
As opposed to traditional BI and BA tools, AI opens up the ability to work with unstructured data, such as chats, videos, charts etc. Chances are the marketing team may not even have knowledge of what unstructured data is available, as it can be siloed by department depending on how it’s used. An audit across customer and buyer facing teams can reveal what’s out there and how it could contribute to the analysis required.
Once that stage is complete, then you need to define the foundation of the data, i.e. how you will strip out personally identifiable information, and other data handling protocols.
This element of data management will require a long term strategy, and close coordination with IT, so understanding what value it will provide to the organisation first, is crucial.
Have a robust process for sense checking in place
Make sure that the resulting analysis is double checked at all stages to ensure the results are accurate and usable. This is particularly important if you’re working with new data structures and sets, as results may be skewed by combinations of data from multiple sources that could lead to overlapping results, as one example.
AI has opened up the ability to work with data across multiple systems, without the need for integration and common data structures, which is great news, but can cause other problems if you’re not prepared.
Nowhere has the idiom Fail to Plan, Plan to Fail been more true!
