Traditional Marketing Funnel theory is based on maximising the number of PEOPLE in the customer journey (the Funnel) so as many people as possible pass through the middle and bottom layers, ultimately leading to as many conversions as possible.
Since the days of ‘Mad Men’, brands have paid for mass media advertising, direct marketing and PR (amongst other strategies) to influence the purchase decisions of people without the benefit of knowing where prospects are at in the purchase cycle.
The below diagram illustrates traditional Marketing Funnel theory that seemed valid in a mass media-oriented marketing environment.
Marketing in a digital world, however, requires a different way to think about the Marketing Funnel.
Marketers must regard the Funnel in terms of DATA collected – not PEOPLE reached. As a prospect – not yet a customer – moves through the purchase cycle (i.e. down the Funnel), marcomms should gather more information about that prospect so each communication gets better and better at converting them to a customer by gathering and using more knowledge about their purchase intent.
The data-driven marketer’s mantra: Quality of data beats quantity of people every time.
This approach turns the traditional Funnel upside down – as shown in the re-engineered Funnel diagram below.
At the top of a data-driven Marketing Funnel, prospects are anonymous as they scan the marketplace for a solution to their problem. Of course through qual and quant research marketers learn insights about consumer attitudes at this early stage of the purchase cycle, and that is vital information to determine branding and advertising strategies.
However, data-driven marketers are constantly seeking hard data that can be used to send relevant marcoms that reflects the prospect’s actual purchase intention. In the AWARENESS phase, the best resource may be 3rd party data that has been aggregated from commercial partners or promotional activities.
The goal for data-driven marketers is to efficiently convert anonymous prospect data (such as TV viewers, mailing list data, website visits, online video viewers, etc.) into 1st party prospect data (email address, location, social profile, purchase intent, etc.).
The strategies to achieve this transition are varied but have one principle at their core: if you are going to ask for someone’s personal information, there must be a ‘value exchange’ so they deem it worthwhile providing their data. No value = no data. Simple as that.
Two critical data points are: what they want to buy, and when.
Knowing these two pieces of information creates a completely new framework to talk to a prospect. It’s not rocket science to realise that for items with a short purchase decision cycle, the frequency and urgency of the communications will be far greater than that for items with a long cycle. Similarly, for luxury/premium brands or high cost purchases, the message and tone of voice will differ markedly from that used to promote commodity items.
When you have a prospect’s contact details, with permission to send messages, and you know what they want to buy and (roughly) when they want to buy – you have the equivalent of marketer’s gold dust.
During the CONSIDERATION phase, the best marketers are collecting 1st party data about their prospects. This enables them to activate data-driven communications strategies that are customised to match their brand positioning with the changing information needs of prospective customers as they move along the pathway to purchase. The net result is to obtain a more complete picture of the prospect so the messaging reflects reality – not some assumptions based on pure demographics at best or guesswork at worst.
Post-PURCHASE is when a marketer is literally ‘information-rich’. Now it’s feasible to predict future buying patterns and use this data in post-purchase marcoms. For brands that sell a renewal-based product or service – such as insurance, auto and telcos – it is not hard to predict the customer’s needs based on historical buying patterns. For products/services whose sales are more driven by variable factors such as price, fashion, weather, or events, it is critical to develop a deeper level of engagement so the first-time customer becomes a repeat customer regardless of the variable forces that can sway them to choose an alternative supplier/brand.
The theory of a data-driven Marketing Funnel is underpinned by the need to capture as much data about a prospect as possible. Which raises the big question of “which technology is needed to support a data-driven comms strategy?” A CRM platform will be the ‘engine room’ that must connect seamlessly with CMS, email, search and display platforms, plus possibly ecommerce, social and mobile apps. The whole martech eco-system will need to capture and store leads from outbound and inbound marketing campaigns, plus manage a suite of inbound comms to a segment of one.
If your organisation wants to develop a data-driven Marketing Funnel,
contact marketingbytes for an initial discussion to discover how we can assist with the strategic planning, technology selection and resourcing required to implement.