Marketing automation, behavioural algorithms, big data, predictive lead scoring and even artificial intelligence (AI) – sometimes it can feel like IT and computational skills are the most important a modern marketer can have.

There is no doubt that over the last decade or so we have been witnessing the emergence of an astonishingly powerful set of new marketing technologies. Some of these technologies, such as marketing automation, are already exerting a powerful impact on the day-to-day business of marketing. Others, notably AI, could profoundly change it in ways we haven’t even begun to consider.

But in our all-too-human rush to embrace things shiny and new, I would argue there has developed a clear tendency to over-estimate the potential impact of these technologies on our here and now, and to over-rely on them once we have them in place.

Over-rated

Potent though the new generation of marketing technologies and toolkits undoubtedly are, research suggests that the marketers who use them often find them to be harder work – and with poorer returns – than they had initially expected.

As marketing technology consultant and analyst David M. Raab reports, marketing automation and lead nurturing were among the poorest performing tools on an effectiveness v difficulty evaluation carried out by Ascend2 and Research Partners.

Conversely, more traditional tactics including content creation, PR and blogging (article writing) were rated much better.

Over-trusted

In a previous blog post Automatic for the People I highlighted the concern that having made a commitment to a new marketing automation platform, more and more businesses – and their marketing agencies – have become over-reliant on them as some kind of ‘magic bullet’ solution.

As I wrote at the time, this over-reliance on automation at the expense of human engagement can be a big mistake. While automation could (and should) be doing a lot of repetitive process-driven legwork for the modern marketer, left to its own devices it is capable of making potentially disastrous errors – from responses which sound like they were sent by machine (because they were) to lead scoring systems which fail to distinguish between the serious prospect and the ‘tyre kicker’.

To some extent these concerns are mitigated by volume. When you are dealing with the huge data sets of multinational retailers like Amazon for example, the algorithms perform more accurately and any mistakes matter less.

But at a more modest level, any business-to-business or small-to-medium company marketing strategy which relies solely on algorithms to drive its engagement and nurture processes is likely to quickly fall short.

Us and them

As so often (but perhaps unfashionably in today’s more extremist times) the answer lies in finding the right balance.

To say that marketing technology is over-rated and over-relied upon is absolutely not to say that it shouldn’t be deployed. Far from it. I would argue that it is almost impossible to imagine a modern marketing campaign without any kind of automation.

But we also need to recognise that human intervention and involvement doesn’t represent a failure or an inefficiency in the system (a very IT viewpoint). Marketers are (or should be) prized by their businesses for their creativity, their empathy, their instinct for engagement and relationship building.

It is only by harnessing marketing technologies to these skills and instincts that we can truly reap the benefits without falling foul of the many bear-traps.

Humans 4 Robots 0

To finish on a practical example, let’s look at four clear advantages we find from manual involvement in the scoring of leads. This is a fairly simply algorithmic process which the majority of today’s marketing software platforms are happy to help you automate, but which on the majority of campaigns we still find it helpful to intervene personally.

  1. Context: raising lead quality

For most businesses, lead scoring only really works if the sales team has faith in the marketing qualified leads they are being handed. The more dud leads they find themselves following up, the less inclined they will be to act on future leads, preferring instead to stick to their own established contacts and existing customers.

By going the extra mile – adding a bit of LinkedIn research, visiting the company website, and understanding the context of the data you are seeing, you as a marketer can raise the quality of each lead and increase the chances of the next stages being progressed properly.

As an example, automatic lead scoring often puts strong emphasis on the recency of activity. But in many markets there can be a long lead time between early research visits and the actual specification / purchase visits. So while automatic scoring might show a low lead score for a single webpage visit, your understanding of the market context, alongside a review of visitor activity from last year or even earlier, means you can identify this as a visit which could be worth more than it seems.

  1. Insight: making connections

In the perfectly automated marketing world, every bit of customer activity and engagement would be logged, analysed and cross-referenced. Unfortunately (or perhaps not), the complexities of the real work mean that this will never happen. Your algorithms might well have access to website visitor activity, email responses, social media discussions and more – but it is unlikely to be able to cross-reference these with things said, read and done in the offline world.

The human advantage here is in being able to draw in data and information from non-digital as well as digital sources. So that while looking at an unremarkable activity event on your marketing dashboard, you can be making instant connections in your mind with a discussion you had last week with a colleague in sales, or something you read in the trade magazine, or with a sample request you recall fulfilling 18 months ago…

  1. Inspiration: learning lessons

It’s not just quality of the leads that is improved by manual lead scoring. It’s also the quality of your future lead generation and engagement.

Because while reviewing daily website visits, email campaign responses, social media engagement and sign-up data, you are inevitably gathering ideas and inspiration for future activity and systems.

Manually monitoring activity and scoring leads allows us to study customer behaviour, visitor patterns and of course how our own campaigns and systems are working

It also provides the opportunity to see new market sectors and opportunities emerge in real time, or perhaps to identify new product applications we hadn’t even thought of.

  1. Creativity: better engagement

To turn prospect interest into customer action, every campaign has at some point to reach out with communications which attracts and engages.

Standardising and automating those communications is one option (and indeed it might be the only option when facing vast global customer volumes). But for smaller business-to-business and small-to-medium company markets, this runs the risk of missing more often than it hits.

By personally reviewing the data that is coming from the marketing software, marketers have the opportunity to think creatively and individually, and to consider ‘how do I best attract this person’s attention?’. Of course that might be with a standard response mailer and follow up sequence. But it might equally be one tailored to their own sector, or an entirely bespoke personal email, or a phone call (or perhaps even a formal letter – who knows?!).

Once again it is only by manually reviewing the data that you have the chance to be creative at these critical points in the customer journey.

Finding the balance

Marketing technologies are powerful, indispensable and here to stay. Very few of us would want it any other way – the reach, insight, speed and efficiencies they offer are priceless.

Used in the right way the software can rid us of repetition and drudgery and free us to offer our businesses and organisations what we do best – creativity, insight and ultimately customer engagement.

But used poorly, whether by over-estimation, over-reliance or both, they can drain resources and blunt effectiveness across the board.

The answer isn’t human or machine – it’s finding the right balance between the two.

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