5 lessons for fast-growing startups

Jozo KovacGeneral

big data marketing automation

This article is based on my presentation at Nexteria IT CLUB, a local event about Big Data Analytics.

Many companies – fashion, travel or SaaS – expand so much and so fast that they fall victim to their own success: in no time, their growth actually prevents them from developing further. I have seen this happen many times.

Since our launch 8 months ago, we have seen a rapid growth, and we are working with passion and dedication to preserve our hard-earned top position.

What are we doing differently? What is the right way to grow a business this fast without losing control?

Allow me to share the 5 lessons we have learned on our way.


Speaking at Nexteria IT Club, March 2016

1. Vision leads, but data follows closely

A clear vision from the word go is a sine qua non for great results. Great leaders set high expectations through their vision – and then they immediately follow by start feeding real data.

A visionary is someone who seems to be able to see the future. While there is no such thing as a crystal ball, visionaries who can consistently predict the future do exist. Whether they are the CEO or the Project Manager, they all share one characteristic: making decisions based on hard data and insights from the real-time user.

2. Having an internal analytics hero is crucial

All sorts of changes, including new promotions and campaigns, are proposed every day. The fastest of the fast-growing companies are making decisions and execute on changes based on real data – both data about trends and historical data about that customers’ past behaviour.

Every company should have a Data Hero, an expert who knows its historical data inside out. Every successful fast-growing company has one already. Screen Shot 2016-04-04 at 16.32.31Excerpt from Skypicker’s Dashboard, Travel Industry

3. Do not scale up without good use cases

After you have a vision and seem ready to scale up, plan good use cases for each stage of the process.

The “trouble” with automated programs is that they will give you almost supernatural powers, and it is good to know what you want to achieve with them.

You will be able to increase product and/or service adoption, raise customer satisfaction, move clients from trial to purchase, and much more. Choose what you want when, and you’ll be sending regular, consistent messages to new clients.

This way, you avoid losing valuable leads, and the great thing about it is that the nurture streams happen on their own, automatically.

cart reactivation triggered events

Example of triggered e-mail campaigns, scenario “Cart Reactivation”

4. Choose the right analytical tool

A good in-depth analytics tool, and Exponea is no exception, makes it possible to take large amounts of messy, real-world data and build predictive models that will find patterns, establish correlations and infer probabilities with enough accuracy to change the world.

Watch Exponea in action

We have been able to help marketers in the travel and fashion industry – I like to use these by way of example because they are fast e-commerce industries that need to respond to triggers in the real time – pick up the best moment to launch a discount campaign for a holiday, and identify emerging fashion trends.

No matter which solution you ultimately choose, remember two things if you are geared for fast growth: (i) a good one will give you in-depth data that you cannot harness from the usual analytics platforms like Google Analytics, Kissmetrics, SimilarWeb, etc., and (ii) it will come as an all-in- one platform so you won’t have to consult 10 different tools.

5. Data structure is key

Thus, you are all set up for fast growth driven by insights about your customers’ behaviour. Or so you’d think. Here’s the final catch: decisions are made in a structured way, and your data should be structured accordingly.

When you are making a data-driven decision, you want to be sure you are using the right data. Here is a representation of what types of data can be accessed through big data tools, and how they relate to business objectives.

big data fremework table transactional data

The following is a non-exhaustive short list of the most common decision-making situations, according to our customers:

• Customer and product profitability

• Customer acquisition and retention strategies

• Customer satisfaction strategies

• Marketing segmentation

• Operations and performance management

• Supply chain and delivery channel strategy B

Structuring your data is both a science and an art. Make sure you don’t end up with a powerful tool that will give you just data. Ideally, your provider will give you on-boarding experts who will help you with the initial setup of data structure. If you want to, you can talk to one of ours now.