What is the tech stack, you ask? It is the solutions stack or data ecosystem used to build and run an application. That application could either be a website, a mobile site, an app, or a combination of both, along with the infrastructure behind it.

For a Careem/Uber/Foodpanda/Eat Mubarak/Cheetay/Bykea model, you as the consumer only see one end of the tech - the one designed to collect information from you to perform the required function. In most cases, it will just be the end user app.

In principle, every decision with respect to a tech stack is something that can have multiple reasons for and against, but everything is centered around availability of talent, ease of deployment, consistency management, version management, processing costs and overheads, and last but not least, end user experience. The last thing you want is an app that is slow because your phone is mid-tier but the language used requires a high amount of processing power, for example.

Behind the scenes there is a whole lot more. The data you are presented with is collected from users who have access to the merchant/rider/restaurant app. Hence there are multiple things at play here. Functionally, you are looking at the following:

1. Consumer app
2. Merchant app
3. Possibly also a rider app if you have Foodpanda/Eat Mubarak type third party riders
4. Databases where everything pertaining to the interconnected actions are stored
5. Systems where all the data is crunched and used for optimisation of processes AND marketing actions

The interesting part is that although a lot seems to be coming from apps that seemingly have nothing to do with marketing, they too provide a plethora of valuable information which can help businesses be better marketers.

In terms of the actual stack behind the functional breakdowns above, you are looking at:

1. The choice of the programming language used (this is a very very important choice to make)
2. The frameworks used in those languages (think of this as a set of rules and functions)
3. Databases used (mySQL, MongoDB, Cassandra)
4. Error tracking and crash analytics platforms
5. Data collection tools in use
6. Product and user analytical tools
7. Data distribution and governance frameworks used
8. Interaction management suites used

While points one to four are purely tech related, points five and eight are hybrid in nature in order to maintain a balance between marketing and tech needs. For example, if your data collection tool collects data but is not able to clean the data or group similar data from two different sources, you may have to find another way of doing so.

Similarly, while analytical tools and platforms are considered to more of a tech/BI play, their ability to enable marketing related reports is critical in order to examine all metrics in one view. In addition, there is a significant need for marketing tools to be able to capture data that was previously mostly discarded. This means that the marketing team need to be more involved in the product selection process as well as use process, especially in the age of product led growth models. These data points are then used to optimise every point of the customer experience cycle to identify points of friction or those that can subsequently be bypassed in the interest of faster conversion or increased sales.

Another key factor taking shape is the impact of historical transactional and behavioural data. Although CRMs tend to hold transactional data, behavioural data which previously was not considered to be high priority, is now extensively used to cross and up-sell products and services.

This is just the tip of the iceberg. A few years ago, the term tech stack meant purely tech. These days, the implicit assumption is that there is now some way to either feed the Mar-tech stack with data or that Mar-tech tools are already pre-planned into the stack.