Here’s another insightful piece on the sessions at MoEngage’s #GROWTHAsia 2020. In this one, the speakers break down the needs of modern consumer marketing, as well as the technologies and tools needed for cutting-edge marketing results.

The session is moderated by Ben Desailly (Senior Martech Advisor) with experts such as Kshitij Hastu (Senior Director of Growth, SEA – MoEngage), Sherry Chao (Senior Solutions Engineer – Segment), Ben Glynn (Head of Growth, APAC – Mixpanel), and Govind Kavaturi (Director of Partnerships – Branch).

Read on to unpack the complexities of a modern growth stack and how to overcome the challenges in adopting modern marketing principles.

How to Define Growth and Growth Stacks for MarTech

Are growth stacks an evolution of martech or something completely different? Ben Glynn quotes product manager Andy Johns at Facebook: “If finance owns the flow of cash in and out of a company, growth owns the flow of customers in and out of a product.”

Thus, growth is about identifying the different tactics and levers that a marketer can apply at every stage of the funnel. After that, it’s optimizing retention to increase the lifetime value of a customer.

Technologies play a role in improving customer experience and conversions at different stages. In the early 2000s, companies like Adobe were pioneers in promoting such tactics for retention and not just acquisition.

So, martech did play an initial role, but after that, there was an explosion of SaaS technology companies, as well as modern e-commerce businesses. Their performance and metrics supercharged the growth mindset.

“Martech paved the way and then Saas and e-commerce businesses really accelerated the growth function.”

Govind Kavaturi says that now we have multiple platforms, such as apps and mobile web. Users have to be engaged across these platforms. That’s how a blend of product and marketing efforts and the platform led to a growth mindset.

How Would You Start to Build a Growth Stack?

Sherry Chao says that first of all, you need a tool for gathering insights about your app or website: something like Google Analytics, for example. A more advanced tool to understand specific users for purposes of segmentation would be Mixpanel.

In terms of communicating with customers, a tool for e-mail is definitely worth using as it’s responsible for a large number of conversions. To optimize for mobile, push notifications and text have to be added.

Beyond this, there are platforms to run ads on sites such as Facebook and Google for acquiring new users. The toughest part is figuring out the tools needed, and the engineering resources to use them all.

Ben Glynn feels that if you have a limited budget and resources, prioritization is critical. It’s best to start with the customer and think of their ideal experience and your marketing objective. Then it’s about finding the tools to deliver that experience.

“Think about that ideal first experience that’s going to drive repeat behavior.”

How to Cut Through Competition and Noise

Kshitij Hastu feels that there are many types of confusion that can prevail. For example: how are we different from the competition, how are segments different, and so on. The question of what tools and platforms to use in such a scenario revolves around looking at them in a silo, instead of the entire growth stack.

When you look at the entire growth stack, you look at the CDP that you need to start tunneling data to the right places. You start looking at overall analytics and automation, and what the data is saying about what actions should be taken. This also gives a view of what’s working and adding value.

“I think having a broader view of the entire growth tech stack is definitely one of the best ways to reduce confusion.”

Looking at the whole picture always clarifies the situation. It also shows the value of delivering the same experience across different channels.

At Which Point Could Machine Learning Play A Role?

Ben Glynn says that there can be two ways to look at this question. There are many areas of machine learning that can be applied across the stack. It can be used for enhanced personalization and efficiency.

  • For machine learning to have an impact, you need a certain amount of data to start with. So, for start-ups and new product launches, machine learning may not be effective.
  • For those with data, the decision is whether to build proprietary machine learning capabilities or rely on outside algorithms. That depends on resources and access to data science teams.

For Kshitij Hastu, “machine learning is only going to be as good as the data you have.”

For lean teams, start-ups, and smaller businesses with an amount of reliable data, there’s definite value in using some out-of-the-box machine learning systems. A/B testing, for instance, can be enhanced.

How Mobile-first Is Changing the Tech Landscape and Growth Stacks

Govind Kavaturi says that first, we need to think about where we are in terms of maturity. For those in new segments and markets, growth stacks may not be suitable at the start. However, mature areas like video formats or eCommerce give you data that can be used with the right tools. “We need to let the data decide what kind of growth stack we need.”

For Sherry Chao, one of the challenges is stitching together experiences on different devices that are used by the consumer. Mobile-first also means having to stay on top of trends such as Apple’s recent app consent rules or the use of TikTok for advertising.

Two issues with the mobile-first landscape that Ben Glynn finds are:

1. It’s hard to build technologies for a great mobile experience.

2. Megabrands like Uber and Netflix have set the bar very high in terms of consumer expectations.

“Mobile-first experiences are about getting on-demand access to services in real-time.”

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