This blog post is aimed at the MarTech (Inc. AdTech) company, Management Consultancy or System Integrator.  

I’ve always considered myself somewhat technical, for a commercial person. I’m not a techie but the relationship between technology and commercial value has always fascinated me.  Understanding how technical capabilities translate into differentiating commercial value. After all, if your selling a technology baed solution and you don’t understand the value, how will you ever convince your buyer of it!  

Where do big data solutions fall down?

They are designed to make it easy for people (users) to ingest, unify, analyse and activate data and by the very nature, lean into ease of use over sophistication and are constantly trying to combine the two. You can’t have the best of both worlds - it’s either ease of use, marketers can use it or its sophisticated, engineers need to build it and data scientists use it.  

Todays big data marketing challenge is not solved by having a sexy looking and easy to use interface.  The problem needs solving at the core, directly within the data layer. 

When does big data become big data

When you’re dealing with at least 10 million records of raw telemetry (not aggregated data) and many different use cases spanning paid, earned and owned media. 

We have one client with several billion records running several thousand queries per month. Lowering query run times by 30x and reducing task hours by 90% was a big deal.

For another client, creating an identity spine and enhancing analytics to enhance their existing capability/stack, without any disruption to their current roadmap, exponentially increased the value of their solution, improving ARR and expansion opportunities.  

Most mature MarTech companies fail to tackle these problems without severe headaches. Specifically with the big data, either relying on software to solve the problem or other cloud base data marts or warehouses. And by adding additional components to enable the data layer the trade off is speed and efficiency, contradicting the reason why you want to solve this problem in the first place, to create a solution that provides a differentiating consumer experience for your clients consumers.  

The solution isn’t just in the software, it’s also within the ongoing management of the data, adding modern techniques to continuously refactor data and project views to enable constantly changing use cases.

So when you think about an end to end process from ingest, unify, analyse, activate, and the stages data must go through in each to support the many marketing use cases, you need a combined software and data management capability to support the processing required to deliver the consumer experience brands are competing for. 

And you must deliver this solution to be cloud and data-centre agnostic, to bridge the AdTech and MarTech world in real-time and to enable interrogation of the data layer without exploding the cloud compute costs.  

But solving the big data marketing challenge from a technical perspective is only the start.  

The challenge today is we’re dealing with a moving target, we’re not given enough time to solve one problem before the next arrives. There are so many things being thrown at us, whether the latest change to legislation or TCF (Transparency Consent Framework) is found to be flawed, or countries coming up with their own data residency laws, depreciation of 3rd party cookies or the organisational complexity at brands that prevent real progress.  

We’ve lost sight of the real problem we’re trying to solve. The consumer wants a differentiating, privacy compliant experience, and they want to be in control of that! Not the large tech monoliths.

So, we have an enhanced MarTech application that bridges the AdTech & MarTech worlds, with a differentiating capability in the marketplace, how do we sell it?

• A question rarely tackled effectively. What sales skills are needed?
• How do we enable your sales and marketing teams, effectively?
• How do we adapt our current sales process to win consistently with this new capability?
• What is the value proposition and value story we take to market?
• What is the differentiating commercial value our new solution provides and how do we train our people to quantify this value and teach our buyer how to buy it successfully?

Without the above questions answered, the chances of you winning deals and growing your business are slim. 

Here are some considerations when thinking about your new Sales GTM Strategy

Pipeline development

• Conversations - what does my relationship first pipe growth strategy look like (insight & human approach). 
• Qualification - how do I qualify in as opposed to out and what are the behaviours to drive toward.  

Value creation

• Impact Story - why buy, now?
• Strategic levers - linked to impact story, virtual levers, when pulled increases value realised.
• Use cases - strategies, programs and campaigns that accelerate speed of levers pulled. 
• ROI model - aligned to strategic levers and use cases (ideally unique capabilities highlighted).  
• Path to value - steps, milestones or projects to realising full value (impact story) 

Sales approach

- behaviour based activities for seller and buyer to follow - increasing probability of positive commercial outcome for both. 

Seller

• Use insight to teach your buyer and tailor your approach, use the value creation approach to guide your buyer / decision making unit toward a positive commercial outcome - predictable forecasting.  

Buyer

• Help your buyer define the purchase criteria, recommend / shape an evaluation process that strengthens your buyers position and coach them to build consensus with the decision making unit - to influence predictable forecasting. 

These are just a few areas Supero can help your MarTech company, SI or Management Consultancy define and execute uponSolving the big data marketing challenge. 

To learn more or just have a friendly chat to swap learnings, drop me a DM on LinkedIn, Twitter.com/Alex_Supero or WhatsApp +44 7951 220113