Intent data has been in discussion everywhere over the last few days. Whenever we got into a discussion with a prospect, the topic that has led to an exhaustive discussion has been “customer intent/buying intent”. Often, they ask “How does your tool identify the accounts that can be targeted now, and the ones to nurture?” More precisely – “does the model always work? Does it not differ from company to company?”
In short, this is a common question that most people in the artificial intelligence circuit get. So, I thought I’ll share my views with you on this matter.
Why intent matters?
Nobody knows your customer better than you. That said, when you have a sizeable number of customers, you will have noticed that most of your customers share some common traits – which at Fiind Inc., like to call “Signals”
Signals can be anything from their IT environment, or the products they recently bought, or where they are in the buying cycle, and more. In other words, the factors that you would look for in your prospective customer – to ensure a great fit.
Based on the above signals (according to relevance for your organization), the shortlisting of prospect begins. The most important factor in segmenting these prospects is “their intent”
Especially, if you are selling in a mature market, where there is a lot of awareness about your product category – intent data can be of great help to you, because you can focus and prioritize on the prospects that are interested in your product and are a great fit too. The conversion possibilities and sales productivity goes up quickly. You would rather talk to someone who has a budget, to whom your product is a great fit and has a high buying intent than to someone who needs a long-time nurture, isn’t it?
How can you best use ‘intent data’?
Intent data is usually a combination of data from internal as well as external sources. Internal data refers to the information from your CRM, marketing automation tools, the CTRs, downloads, etc. External data refers to information available online across various sources.
Our customers tend to use this internal data for Predictive lead scoring, automated customer outreach, account based marketing and more. At a more granular level, they use it for personalizing email marketing and content marketing campaigns, pitching the right cross-sell for the right customer, targeted advertising, etc.
You can typically start with a fit-first approach, and determine if an incoming prospect resembles your likely buyer. Then, you can layer them into a behavioral model based on marketing automation data, CRM and advanced machine learning to keep your plan for the account dynamic. Bringing this kind of automation within your martech stack will reduce the sales-marketing gap significantly.
Factors to consider when using intent data
The availability of intent data is based on the maturity of the industry you are into. For example, lot of intent data is available for the software industry. But if you are operating in a niche market – the fact of the matter is – there might not be enough companies for a good-enough sample size. To be clearer, if there is no data, there is no output.
Now, let’s say you have enough data – we have learnt that when we build predictive models, intent is a key component but it would matter to your specific case only if you also take your product fit into consideration. For example, if a model yields a low fit score, but a high intent score – the sale propensity with respect to your product is going to be low. Be mindful of the factors that go into intent data indication.
Never choose vendors who are skewed strongly towards intent models or towards fit models. You need both. Evaluate your AI vendor. Ask questions, see it for yourself – validate.