Crossing Your Data Science Chasm

Scenario — You’re an up-and-coming ecommerce/SaaS startup. You’ve got your site up, you have A/B tested your message, and you’ve got your SEO, and social ad buys. You’ve set up your email drip campaign and reminders. You also have basic BI reporting telling you channel traffic and conversions. Traffic is decent, and revenue is growing. You are likely in the initial growth phase; you’re flying on skates and business is booming. You think you’ve found your voice; you’re expanding; you’re reinvesting for more growth; you’re going big on marketing and things seem to be going great for a while.

Then, all of a sudden, traffic slows down. Ad buys are not as effective as they used to be. Promotions still bring a spike, but they are temporary and short-lived. Overall growth can’t seem to get over the hump, and LTV is in decline, which means it would take twice as long to be profitable. Why? While there could be many reasons, the most likely cause is that inevitably the same sales and marketing engine that has worked in the past have started to slow down over time. A clear case of “Law of shitty clickthroughs” as explained by famous growth hacker turned VC Andrew Chen.

Over time, all marketing strategies result in shitty clickthrough rates.

— Andrew Chen

We’ve all heard the term “crossing the chasm”: all successful technology products eventually have to cross the chasm; to leap from early adopters to mainstream consumers. Finding product-market fit is hard, and making this leap is not trivial.

Let’s say you’ve now taken your first beachhead, doesn’t matter if you started from scratch with a novel idea to start selling online, or you are in the midst of digitally transforming your offline business. You’ve built up a loyal following who believe in your cause and your brand. To get to the next level, you’ve got to look closer at optimizing internal operations as well as opportunities to expand. It’s more important than ever now to have all the information you need to make the right decision and make more winning bets than losing ones. Now you’ve got to think deeper, exercise data muscles you’ve never thought to use before. You need to know who your customers are, when are they likely to purchase, and what are they going to buy — in other words, going from knowing “what happened” to “why it happened” and “what will happen” or in short, predictive analytics.

This is where Data Science and Machine Learning come into play.

But wait a minute you say, you have already looked into this but couldn’t find reasonable out-of-the-box solutions on the market. Perhaps you tried to hire a data scientist but found out it’s hard to find the right combination of talent/cost/skills? Or maybe you think the metrics you have and your decision making are good enough because they’ve gotten you this far, right?

This is what I’d like to call the Data Science Chasm — the gap between web analytics/BI reporting to predictive analytics. It’s like going from Google Analytics to Google Deepmind (of AlphaGo).

The typical choice of Buy vs. Build breaks down because of the inaccessibility of data science today. Due to a shortage of talent, scarcity in business precedents, and the lack of affordable solutions, SMBs often find themselves shut-out from the data science and insights that could make the difference.

There are products such as Google Analytics, Optimizely, and Mixpanel that have tried to fill the gap on web analytics and reporting. However, advanced analytics and machine learning — and more generally, artificial intelligence — are still well beyond the reach. We want to change that. We believe in the human element of data science. To effectively cross the chasm, you need access to expert advice to outline and explain clearly what steps to take to get there. On the other end, you need a person to interpret and hold accountable the results, as well as design the go-to-market that makes sense for your business.

To get you started, we present here an analytics roadmap to help you navigate what you need to get to the next level. Let’s cross your #DataScienceChasm together.

Descriptive Analytics

With plug-and-play tools, the hard part is diagnosing issues. When a metric spikes or plummets, it requires knowledge and experience to understand why and to get back on track. Business intelligence and reporting can tell you what happened, but to know why you will need to go deeper.

Descriptive analytics sits right on the edge of the data science chasm — SaaS tools can solve pieces here and there, but many problems require a seasoned eye. Several useful analyses and techniques include:

• Seasonality and historical trends
• Product purchase cycle
• Promotion and discount effectiveness
• User segmentation and clustering
• Funnel conversion analysis
• Market basket analysis

Large companies will often have many in-house analysts dedicated full-time to the above areas to optimize and accelerate their businesses. At LinkedIn, every product line has extensive user segmentation and purchase funnel optimization. For example, you are likely to see a different subscription product offered when you check out based on your profile. For smaller companies, there are no resources for hiring full-time analysts. The chasm needs to be crossed by DSaaS (Data Science-As-a-Service) services that provide analyses and algorithms that deliver immediate results without spending big on R&D and shorten time to insights.

Predictive Analytics

Effectively crossing your chasm into hyper-growth is all about knowing where to allocate your resources. What if you knew which users were more likely to purchase your products? What if you knew the upcoming few weeks were going to have a massively outsized effect on your sales this quarter?

The higher probability you can have of certain events or outcomes, the higher the chance of surviving another year. Some predictive analytics include:

• User acquisition propensity models
• Repurchase or churn models
• User lifetime value predictions
• Sales forecasting and promotion planning
• User lifetime value (LTV)

Large companies have even more senior data scientists working on these problems. Data is a great differentiator. If you can leverage data effectively to understand your business and know where to focus, you will be in the top 1% of companies.

Prescriptive Optimization

Prescriptive Optimization is the holy grail of data science and machine learning. Imagine a fully automated, self-learning, low code deployment of AI connected components that react to your business based on real-time purchase behavior, and action-based protocols.

AI handles the ordering, shipping, pricing, emailing, and customer service that delivers optimization to all corners of your business.
Some key components you would likely need are:

• Dynamically updating product hierarchy
• Purpose-driven product categories based on purchase behavior
• Smart pricing models
• Autonomous AI that updates rules and flows as it learns

In the coming weeks, we will spend more time diving deeper into Data Science topics to give you a simple explanation of how it applies to your business. Please leave a comment if there’re particular topics of interest you’d like to see!

Wherever you may be in the analytics roadmap, there is always room for learning and improvement. The data science journey is a long one but rocketship growth, and ultimately, a unicorn business is a reward worth working towards. If there’s anything we can do to help, please drop us a line!

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