Fixing the blind spots in your digital transformation efforts – Information Age

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Organisations have been prioritising digital transformation for decades, and all that work comes with a hefty price tag. By 2023, IDC predicts that companies will have spent a staggering $6.8 trillion on digital transformation efforts.
But what’s the return on that investment? Many companies have no idea. They don’t have visibility into how their customers are using their digital products and they’re guessing at what digital experiences to build next, which bets are working, and how all of this drives business outcomes. The secret is to remove the blind spots.
Here are three actions every executive needs to make to optimise their digital products, accelerate innovation and ultimately increase the value of their digital transformation investments.
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There’s often a disconnect between what your customers say they want to do and what they actually do. That’s why it is critical to have visibility into your customers’ product journeys. For example, what actions in the product lead to a repeat user? Where are your biggest drop-off rates? Where are users stalling in the purchase process?
You can use these insights to optimise your digital product. Facebook famously discovered that the key to great user engagement was adding seven friends in the first 10 days of signing up. The company re-designed its product experience around this insight, and we all know that turned out to be a success.
But the tricky part is getting your hands on this product data – the sheer number of data points needed to join, analyse, and correlate customer actions to outcomes makes this incredibly complicated. Companies have tried (and failed) to use web and marketing analytics tools to pull this off, but these products weren’t built for the scale and complexity of today’s digital products. Instead, teams need to utilise product-specific tools that leverage machine learning and offer real-time insights.
If digital products are essential to business growth, and data is critical to building great products, who owns the data? The correct answer is everyone.
Yet, in many organisations, data science teams are still responsible for aggregating data, doing the analysis, and reporting back to other parts of the business. This operating model creates significant lags in productivity, with even simple data requests taking multiple weeks to execute and deliver. In today’s fast-moving world, this is enough time to be disrupted by a competitor.
Data is infinitely more valuable if it can be leveraged by cross-functional stakeholders spanning product, engineering, sales, marketing and customer support. With the rise of no-code data tools, your employees don’t have to be data scientists to be data-driven.
We gauged the perspectives of experts in data science, asking them about the biggest emerging trends in data science. Read here
Customer acquisition will always be a fundamental part of business – this will never change. What is changing is the split of investment between customer acquisition and retention. By removing their digital blind spots, businesses have the ability to engage with customers more easily over time, shifting investment away from securing one-time purchases and toward building lifetime customers.
This starts with understanding how customers are successfully using your product – which features do they use most, which user paths lead to a sale, etc. — and then applying those insights to other users. The process isn’t instant; it requires rapid A/B testing to get right. But this change in strategy will increase digital transformation ROI and create a more efficient growth model — the cost of acquiring a new customer is five times the cost of retaining an existing one.
Digital is no longer just an enabler of business; it is the business. But if decades of digital transformation efforts are going to be worth the spend, business leaders need to shift their focus from transformation to optimisation — and data is the bridge.
Written by Daniel Bailey, vice-president of EMEA at Amplitude
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