AI/ML in Go-To-Market (GTM)

AI/ML in Go-To-Market

Traditional marketing strategy focuses on keeping the funnel healthy on a continuous basis through the regular channels such as paid media, email, blogs, webinars etc. The initiatives typically take advantage of available data analyses to retain market share, make incremental growth moves, increase ROI or a combination of all of them.

On the other hand, Go-To-Market (GTM) strategy typically focuses on pushing new products or expanding into new markets (or verticals) - specifically around the time that such launches are scheduled for. The challenge with #gtm campaigns is the scarcity of a historical perspective – there is limited data and analyses available for guidance. In an unknown terrain fraught with risk, the business has to grapple with many alternatives. A lot more experimentation is needed to understand the implications of particular decisions. Often, this leads to a chaotic roll-outs and campaign failures. A methodical, yet creative approach is required to navigate the risks steeped in ambiguity. And the approach should lead to quick learning and adjustments. In this quest, businesses that deploy #ai solutions in their GTM strategy can allay some of the uncertainties and come out ahead in the competition to grab market share.

There are numerous ways in which AI/ML can be useful in GTM campaigns. With the help of AI/ML, the business can achieve,

1. Customer segmentation based on propensity to purchase

2. Improved sales forecasting

3. Effective A/B tests and experimentation

4. Improved ROI on ad spend

5. Improved CAC for overall spend

6. Efficient inventory management to avoid overstocking and stock-outs

7. Pricing optimization experiments

8. E-mail campaign efficiency

9. Personalized customer on-boarding

10. Larger scale of experimentation

AI/ML allows for fast learning at a scale that is impractical for humans to achieve for each of the topics above. Needless to say, AI/ML works only on the rules created by humans, but brings speed, agility and scale. It is therefore a balancing act between human cognition and AI/ML analyses that can help conquer some of these challenges. As we learn more about uncharted domains, AI/ML will be an indispensable tool in the future.