Metric Matters: Nitesh Sharoff, growth marketing & analytics consultant

In our first Metric Matters interview, growth marketing and analytics consultant Nitesh Sharoff talks about the data analytics mistakes companies make, and how to avoid them

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Data analytics is critical for any businesses hoping to be more competitive today. After all, data-driven organisations outperform their non data-driven peers to such a remarkable extent, they’re 23x more likely to acquire customers.

 

 

Data analytics is critical for any businesses hoping to be more competitive today. After all, data-driven organisations outperform their non data-driven peers to such a remarkable extent, they’re 23x more likely to acquire customers.

From improved efficiency to better financial performance, there are countless benefits to using your data correctly. However, for all we’ve spent the last decade going on about the promise of ‘big data’, there are still significant barriers to entry. Today:

 

 

Despite business leaders chomping at the bit to embed data analytics into their team’s workflow and culture, there’s still an awful lot of work to be done. And many don’t know where to start.

So, we caught up with Nitesh Sharoff, growth marketing and analytics consultant, and founder of Growth Runner, to talk all things data analytics, how companies can cultivate a more experimental mindset using their data, and some of his top tips for driving growth.

 

 

Data analytics: The key mistakes

“When companies implement data analytics, you tend to see two key mistakes,” says Nitesh. “Half of the companies I work with feel like they don’t need behavioural data at all – especially if they’re very tech driven. Developers particularly find analytics to be a pain to implement, and just want to skip through the process because it slows them down.

“The other big mistake I see companies making – particularly large companies – is that they decide to track absolutely everything under the sun: From people interacting with images, to hovering over buttons. But that data just never gets used to fuel anything.

So what’s Nitesh’s best advice for rolling out data analytics practices, company-wide?

 

 

“I ask people to start by thinking about their biggest KPIs, and then break them down into smaller KPIs,” he explains. “For example, if the main KPI is profit (and it often is), that’s made up of loads of smaller metrics, like: Visits x Conversion rate (CR) x Average Order Value (AOV). Once you’ve identified your main metrics, you can then have someone like myself or anyone in data collection to think through: What do we need to collect to fuel those decisions?

Depending on the size of the business, core KPIs can be broken down into five or six KPIs.

“What works really well is having each department own one KPI,” says Nitesh. “A lot of smaller factors are going to be influencing that KPI. But, essentially, that department’s in charge of that one KPI, and all the metrics that make that up. This gives them a bit more ownership. And once there’s accountability, you start seeing performance kick in straight after, and you’ve got a team who’s empowered by their data, rather than confused by it.

 

 

He goes on: “So if it’s product owners, for example, in their weekly meetings with the developers or marketing team, they can start talking about the data related to their specific KPI. This helps the team align to one big core goal, and data literacy is increased throughout the business.”

 

How to find the right data?

Nitesh’s tagline reads: ‘I grow businesses by collecting the right data’. So, we ask him to define what he means when he talks about ‘the right data’.

“Every organisation has goals. And whatever their goals are (most of the time it’s revenue), they need be able to break that down into a few data points that say a lot,” he explains. “There’s data points that are relevant, but shouldn’t be primary data points. Like bounce rate. Bounce rate is relevant if you’re doing content, but it’s a subjective metric and very hard to manage. Whereas a KPI like average order value (AOV) is easier to influence as there’s concrete action you can take – like upsells and making sure products are coming on time – that will move the needle.

To find the ‘right data’, you need to pick the best KPIs, and have each department own their own KPI.”

 

Building an experimental mindset

Once you have the right data, the right KPIs and each department is empowered by the ownership of their own data, then you can start experimenting. Today’s most famous hyper-growth organisations put their success down to developing a constantly-experimental approach, but how can regular companies make the transition to being more experimental in their day-to-day?

 

“I think it’s about embedding an experimental mindset into the company,” replies Nitesh. “And understanding that experiments don’t have to be complicated. They can be something as simple as testing different variants of the checkout. You look at large companies like Net-a-Porter, which have a large amount of traffic coming in; if you do one small tweak at the checkout and get it right you’re potentially going see something like $50,000-60,000 more profit in just one day. So these small tweaks can make a huge difference, and you should absolutely be testing them.”

READ MORE: My best growth hack: Nitesh Sharoff, growth marketing and analytics consultant

Probing the data is another thing that’s critical for growth. Once your team is empowered to fully analyse their data, you can start to understand exactly what’s going on in it.

[Once you’re fully analysing your data] you’re going to see issues in there and no one’s aware of. Really tiny issues, like a specific browser is failing to do something. If there’s an experimentation mindset, someone’s going to be probing the data continuously to try and build out an experiment.”

Learn about how AI can supercharge your KPI analysis so you never miss an important metric again.

Nitesh strongly believes that growth requires both an experimental mindset and a full understanding of your data. “It’s about having an experimental mindset, combined with the departmental ownership of a KPI: Those two things together are a recipe for success.”

 

Google Analytics cheat codes

Before he has to get back to his growth experiments, we ask Nitesh to share some of his ‘cheat codes’ for his favourite analytics platform: Google Analytics.

“Google Analytics released a brand new product a couple of months ago: Google Analytics 4, which I think is great,” he replies. “Exporting your data in Google Analytics used to be quite tricky, especially on the free version. But with Google Analytics 4 you can export data as you please. This has given users a lot more freedom with their data. Now you can pull it out, you can distribute it, you can own it, you can blend it with other data, and you’re able to get a much clearer picture of user behaviour. And this new event measurement tool also combines apps and web together. So my main ‘cheat code’ would be making good use of these new capabilities.”

He continues: “Custom channel groupings are one great feature on the traditional Google Analytics that’s often overlooked. Custom channel groupings allow you to customise the rules for grouping your traffic sources. So, for example, you could segment out branded keywords versus non-branded keywords. This will let you know if users are arriving on your landing pages from branded or non-branded keywords, as opposed to paid search.”

 

READ MORE: In part 2 of Nitesh’s interview, he covers his best growth experiment to date.

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