Today Predictive analysis is being leveraged by B2B Marketers to identify high potential buyers in early-stage, to decode their complicated buyer’s journey, to deliver an accurate and highly personalized message, and to impact ROI for better.
“Technology has reached the point that you can predict the future outcome of operation by studying the historical data and pattern of it.”
Predictive analytics supports this statement, and the concept is exactly the same.
In this blog post, we will learn the role of predictive analytics in B2B marketing to improve marketing ROI.
“Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.” – According to SAS, leading analytics software solution provider.
Let’s start with another question: With what end goal every B2B Marketer is wake up in a mind?
Let us answer. “To push a high scored lead to sales” – every marketer strives to get the lead ready to talk with the sales. The problem lies in a statement ‘a marketer strives to get the lead ready’, whereas the statement should have been like, ‘a smart marketer identifies the sales-ready leads – he doesn’t push, either force or assume.’
He identifies the lead.
Since the 2/3rd of the buyer journey is completed online by a buyer itself, it is not a sane move to ‘assume’ buyer’s position in the buyer’s journey and his knowledge about the specific product. Some buyer knocks the sales door in a short period of time being nurtured in pipeline whereas some might take a long period to make a decision and need more content, demos to support their decision.
The buyer journey is not as simple and linear as it used to be.
The buyers are empowered with advanced technology. They are agile and hard to decode. They are active digitally and send numbers of signals every moment that is only swelling the database of marketing agencies.
In this data chaos world, the predictive analysis came as an effective solution. That enables marketers to be strategically at possible touchpoints before a buyer be there.
As stated above ‘predictive analytics use big data (B2B actionable data) and machine learning to predict the future movement of a prospect by analysing the historical data.’
To be ahead in the journey, to avoid bad personalization and to use marketing resources and budget on right accounts and prospects – predictive analytics is must needed stack.<--!
It is obvious that the next technology innovation will be AI and Big data centred. AI is being widely used in B2B Marketing, and predictive analytics is just a part of it. AI enabling marketers to dig down big data at scale, personalize and automate marketing at scale.
Data is a good storyteller.
Just forget all marketing jargons for a while and tell us what is the most important thing that makes a marketing campaign successful? – ‘Timing’
Identifying a pain point of an account at the right time enables marketers to bring the account in a sales pipeline early in a research stage.
We all are a consumer at some point, let’s think from a consumer perspective, we hardly plan in advance what to do if our AC gets broken down, we do what we are supposed to do when we actually get our AC broken down.
Businesspersons are human too. They follow the same tendency. Most of the business decision-makers are ‘actively’ researching a solution as soon as they start facing a problem.
If we are getting intense signals from a group of prospects that share identical technographic detail, we could say the signals are coming from the same account. This is high time for that account.
Today a marketer does not need to make those anonymous prospects fill landing pages and enquiry form. The high-level goal of tracing down that signal and leveraging the data is to identify accounts that are relevant to the campaign.
In other word - we can say that predictive analytics enables ABM and ABM enables the laser-focused targeting on high revenue-driven elements, i.e. accounts with highly personalised marketing and data-driven experience.
Predictive analytics + Intent signal > TAL > Segmentation > ABM Experience > Account Scoring > Sales Enablement
Let’s see how predictive analytics can be beneficial to make ABM successful:
Segmentation of accounts with respect to their needs, pain points and intent is crucial to personalise the campaign.
Identifying aggregate behaviour signal of an account early in a buying stage enables marketers to create a data-driven content experience for prospects.
An account is a group of decision-makers. In B2B a group of decision-makers i.e. DMU talks in a chorus to the sales.
Although, we could not give less weight to the role of the individual prospect that research independently. A prospect’s role in DMU is wary according to its persona and influence of external factors. Sourcing demographics, firmographics and technographic data for every prospect in the account are equally crucial as sourcing data for an account as one.
Predictive analytics model enables marketers to create look like accounts and behaviour patterns analysing historical data that help marketers to decode the pattern of interaction for specific prospects and account to create content that engages the prospects.
Prospects are agile and so their behaviour against the content. To make sure you are delivering the content to the ready to interact prospect, marketers require content engagement data.
With predictive behaviour, marketers could agree on the right time to deliver the content, to contact the prospect and to ask the sales team to consider the account as ready to buy.
Account scoring gives visibility to ready to buy accounts. Predictive data with actionable lead database enables marketers to score account accurately so the content personalization on a high level could be possible.
Account scoring also enables the sales process for high scored accounts and initiated the re-nurturing for an account that is underperforming in terms of intent and interest.
Marketing is a work of great collaboration. We cannot succeed by focusing on one tactic and ignoring the other one. While predictive analytics works fine, it is marketer’s responsibility to leverage other effective tactics like intent data and marketing attribution.
It sounds good to include all the terms in marketing strategy but it takes great efforts to work with them in collaboration.
What is yours go after ‘data’ strategy?