First, we must start by pointing out what makes Data & MarTech differ from AdTech. AdTech (Advertising Technology) comprises the conventional pillars of digital marketing – campaigns, display ads and search-engine marketing (SEM), programmatic media, Real-time bidding and other familiar terms.
Specialized AdTech also comprises platforms that help with attribution, verification and ad viewability measurement.
MarTech, on the other hand, is more about forming intelligent connections between brands and customers. It extends beyond customer acquisition and is the backbone behind interacting with an existing customer base — e.g. using personalized marketing, audience management, A/B testing, feedback surveys, descriptive and predictive analytics etc.
MarTech, when powered by sufficient data, is essential for Customer Relationship Management (CRM) and Marketing automation.
One can say AdTech is a subset of MarTech, and this can sometimes be confusing so, here are a few pointers to differentiate both at a glance:
|Role||Refers to all software used for conveying, displaying, focusing and controlling digital ads.||Comprises of tools and technologies to manage marketing processes, customer journeys, customer relationships and customer analytics.|
|Reach||Focuses on wide reach, uses a one-to-many approach where ad recipients may be unknown.||Revolves around a “B2B2C” model where technology is used to directly reach specific individuals identified by their customer profile.|
|Targeting||Largely focuses on using cookies augmented with 3rd party and behavioral data to build campaigns.||Taps into a combination of first-party data, personal identifiable information (where provided) and 3rd party data sources to personalize each customer’s experience.|
|Data management||Does not require existing customer data.||Requires integration between a brand’s existing customer data repository and the Marketing Technology, to drive continuous engagement and retention.|
|Channels||Dependent on Paid media (display, video, search etc.)||Typically uses owned channels (e.g. CRM, Customer Data Platform, Audience Manager etc.) to guarantee a constant view of customer activities and insights.|
|Audience footprints||Tracks customer activity to the point of acquisition.||Tracks customer activity on an ongoing basis.|
|Insights & Analytics||Focuses on demographic and technographic data.||Focuses on behavioural data and tracking possible shifts in customers’ behavioural patterns, interests and intent.|
The average brand marketer today understands how digital campaigns are to run via paid media. Here, the campaign manager’s job ends at the point of customer acquisition.
For example, Advertising Technology may power a Customer Acquisition campaign for a bank, and analytics may show insights such as:
- Number of successful account openings.
- Drop-off points in the customers’ journey.
The bank’s CRM may also track each customer’s transfer and withdrawal activities.
What they won’t know however is:
- Each customer’s personal finance preferences outside of the bank’s view.
- Each customer’s interests and buying patterns.
- Each customer’s spending airtime top-up frequency outside of the bank’s channels.
Data & MarTech helps reveal to businesses, data points they don’t have on their current customer base in order to provide personalized experiences.
In this example, the bank may be struggling to get customers to adopt it’s USSD shortcode for airtime top-ups and bill payments. Data & MarTech may show that these customers actively buy airtime using other channels, thereby helping the bank identify blind spots and changes required.
To achieve results, data from existing customers must be enriched with data from other sources. Machine Learning is then applied to study consumer behaviours, while AI automation makes predictions to drive these consumers to take desired action.
Apply this same logic to a ride-hailing service like Uber and you have this scenario:
- How many users installed the mobile app.
- The channels through which these users were each acquired.
- How many of these users became active weekly.
Data & MarTech insights
- Each user’s preferred routes, payment methods and ride frequency.
- The effect of promo codes on each user’s decision making.
- Geographic data per ride, to predict fare estimates based on a region’s traffic patterns.
The major difference is, Data & MarTech helps generate behavioural insights on an ongoing basis which helps to
- Personalize the experience of existing customers
- Create lookalike models of existing customer profiles, for new acquisitions.
In the Financial Services Industry, Data & MarTech is the key to
- Acquiring account openings from individuals with high lifetime value.
- Profiling credit-worthy individuals for microlending services.
- Profiling individuals in need of insurance policies (car owners, property owners, business loan applicants etc).
- Profiling members of the Informal Sector for Micropension schemes.
It also drives financial inclusion by ensuring each of these consumer profiles can be reached online and offline, irrespective of literacy levels and locations.
The benefits of Data & MarTech equally extend to trade marketing, where brands require a system to make automated payouts to retailers and consumers in their various loyalty reward schemes.
Thanks to Machine Learning & AI, consumers who have been profiled as existing customers may receive personalized offers from brands through interactive web & non-web channels, and have promo rewards automated to each individual, either as airtime or bank transfer.
In Africa, few companies have robust customer software and rich data analytics capabilities; none have innovated with the African mobile user in mind. Terragon Group, a Nigerian data analytics and marketing technology company, is the rare exception.
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