This is why plenty of people remain uncomfortable about the prospect of applying it to anything other than financial or campaign-related matters. Conventional wisdom resists the treatment of workers as utilities, and doesn’t like reducing productivity to simple facts and figures.

But this is based on a myopic and outdated view of what the analytics process involves. It doesn’t fully supplant human opinion, nor does it render shallow judgments about workers. Instead, it’s merely a tool: something to supplement human efforts.

Used well, analytics can significantly improve team productivity and performance. How does this work? Let’s take a closer look at this core element of modern business:

They can help efforts to raise morale

Team productivity is about far more than the qualities and skills of the individuals involved. It’s also about how the team members feel, and how they interact. Morale — both individual and on a team basis — is a mission-critical concern, because an unhappy team will struggle to perform and become stressed very easily.

So, how can analytics help you boost morale? There are several ways:

• They can showcase successes. It’s always important for a business to celebrate when things go well, but there isn’t always some obvious profit figure to bring up. With analytics, you can get a more granular look at how things are going, and pick out the precursors to that type of success eventually arriving (the small wins). For instance, you could celebrate a significant uplift in search rankings for important terms, knowing the corresponding uplift in conversion that’s likely to follow.
• They can highlight employee strengths. People like being praised, and while you should certainly look for weaknesses to improve upon, you should also find the strengths to highlight. It might be something as simple as one employee yielding more value per hour, hitting a certain number of tasks completed in a day, or getting exceptional comments from customers on a consistent basis.
• They can guide feedback surveys. Getting full feedback from employees is vitally important, because no amount of analytics can tell you how people feel — but your data can help you create better surveys. For instance, if you notice that a particular process is quite slow, you can ask specifically-related questions, then use the answers to make meaningful improvements that wouldn’t have been possible had you not known about that issue before consulting people.

They can identify operational roadblocks

Digging into the metrics of your everyday operations is a great way to glean insight into the holdups: the snags along the way that prevent everything from running optimally smoothly. For instance, you might notice that one of your internal processes is taking markedly longer than comparable processes — closer inspection might reveal an easy-to-fix bug.

Analytics can also help you monitor where employee time is going, information that can be very informative when it comes to attitude. For instance, you might discover that one of your employees whose role is to write copy actually spends a lot of time handling HR disputes, or that your head of finances is consistently distracted by requests from your design team.

In response, you can try some classic A/B testing when it comes to team composition and organization (provided you know how to interpret the results). Make some alterations, let people work for a few weeks, then compare the analytics of that period to those of the period preceding it. Keep doing this, and you’ll figure out the team arrangement that gets the most work done.

You can also use this data to identify areas that warrant investment in automation. Losing a lot of time handling admin for your scheduling? Try something like Calendly or x.ai to make your meeting easier. Picking through payroll on a manual basis? Given modest SaaS pricing, something like Wave’s payroll software will more than return its value given the chance.

They can support clearer incentives

Properly incentivizing performance is essential for keeping performance at a high level year-round — without it, your employees will start taking opportunities to lessen their workloads, lowering their productivity and paying less attention to what they’re doing. It won’t even be intentional in every case. Dedication is a hard thing to maintain, and people can burn out.

Because a well-implemented analytics system gives you a deep and broad perspective on everything that’s happening with your business, you can discover which elements are going well and which are underperforming. You can then set simpler metric-based incentives using the S.M.A.R.T system, e.g. improve metric X by 10% in two months and receive reward Y.

It’s far better than asking for general improvements and gauging them subjectively, as employees aren’t likely to respond to that approach. It’s not pleasant to put a lot of work into something but have a promotion denied because your boss doesn’t believe in you. Far better to let the data speak on your behalf.

Implementing a system for collecting and parsing comprehensive analytics won’t solve all your problems, nor will it push anyone into redundancy — but it will give you the added insight you need to make your team members happier, less stressed, and more motivated. Why wouldn’t you want that?

The post How Analytics Can Help Improve Team Productivity & Performance appeared first on Reflective Data.

In ideal circumstances — provided you have the resources, the time, and a plan for separating the wheat from the chaff — you’ll bring in data from all possible sources (your internal analytics, market research, and customer surveys, to name just a few). But how should you structure the analysis efforts? Not every area or metric is worthy of granular investigation, after all.

Well, there’s a whole host of elements that warrant attention, but here we’re going to look at purchase data (everything you glean from the orders your customers have placed). Analysis of this data is absolutely essential to your operation — here’s why:

It will help you adapt to consumer habit changes

The consumer market moves forward, sometimes slowly, but never falling entirely static. New products are released, existing products pick up steam due to promotion or word-of-mouth recommendation, and previous hit items fall out of favour or are discontinued. The more rapidly you can adapt to the changes (and even predict what’s just over the horizon), the more strongly you can take advantage of them.

These changes, of course, don’t happen out of nowhere. A product won’t generally go from popular to written-off overnight — instead, it will gradually lose its popularity before eventually reaching a tipping point at which it might as well no longer be sold. By analysing the purchase data of your customers, you can discern which products are rising or falling in the ranks, and draw inferences about what product types are gaining or losing value.

You might see that sales of skateboarding accessories have risen steadily throughout the last year, for instance, in which case you’d have good reason to think that it might be a niche worth investigating — and if you check Google Trends and discover that interest is rising across the board (for whatever reason), you could make a concerted effort to target skateboarders.

Patterns support enhanced marketing tactics

Marketing is a core concern for retailers, particularly in the online realm where you can’t rely on people merely happening upon your store and wanting to shop there. The more efficient you can make your marketing, the better its ROI will become — you’ll sell more, and spend less to win those sales. And since digital marketing is inherently suited to complex targeting (relying on smart segmentation of customer sets), customer trends are hugely valuable.

For example, if your purchase data shows that shoppers from a particular region or country have spent 35% more on food products, you can dig deeper into that stat to figure out some tweaks to your marketing. Could you market other food products to those people? Is there a specific reason why that area is so concerned with food? There could be a new hit cookery show, or a big trend towards healthy eating.

If you identified the latter as the culprit, you could take advantage by marketing other health products to people in that area (you could even use a product discovery tool like Product Hunt to find more). The more you know about customer habits, the more sophisticated you can get with persuading them to spend even more with you.

The data is relatively straightforward to collect

Some types of data (e.g. customer feedback) are somewhat laborious to collect, because you need to incentivise them somehow and then actively chase them — and even then, you might have a dataset that’s both limited (most people prefer not to comment) and biased (the people most likely to provide feedback are those either very happy or very angry with your service).

Purchase data, though, requires next to no effort to collect. No matter what platform you use, you’ll end up with basic purchase records (they’re necessary for customer accounts and support requests). What’s more, if you use something with rich native data (the Shopify ecomm service has GA’s Enhanced Ecommerce features running out of the box, for instance), then you’ll have tagged funnel stages at your disposal — possibly without even realising it.

Will any ecommerce solution give you a flawlessly-parsed analytics dashboard from the outset? No, of course not — that’s why you’re best served finding someone to help you get everything usefully tagged and categorised. But all the data is there and ready to use, not costing you anything or causing you any strife. Why wouldn’t you make the most of it?

Loyal customers deserve additional effort

Customer loyalty is of paramount importance to sellers of all kinds, but particularly ecommerce sellers. It’s tough to stand out through pricing or product selection in the digital marketplace, and winning someone’s loyalty will give you the edge regardless, so you need to prioritise it. But how do you know which customers to focus on? Simple — your purchase data will tell you.

By checking your customer histories, you can pick out those who have placed the most orders and/or spent the most money with you, then make an effort to serve the needs of those customers. You can reach out to them for direct feedback, ask them what improvements you could make, and even offer them incentives to spend more and/or bring you referral customers.

When you don’t pay much attention to what your customers buy or how they shop, you can’t go the extra mile to cater to those with the most value. Keep loyal customers happy, and you’ll reduce your churn rate, convert on a more frequent basis, and achieve remarkable stability.

For all of these reasons, putting in the time to closely analyse the purchasing habits of your customers is a highly-effective way to improve your marketing, adapt to changing circumstances, and cultivate immense customer loyalty. If you’re not doing this type of analysis already, what are you waiting for?

The post Why It’s Essential You Analyze The Purchase Data Of Your Customers appeared first on Reflective Data.

Visualizing data is all about parsing it and presenting it in a fashion that reveals the underlying trends, turning those raw metrics into meaningful stats and patterns. It’s valuable in many ways, but something you may not have considered is the extent to which it can better equip a brand to provide exceptional customer experiences.

How exactly does it have this effect? Allow me to explain. Let’s take a look at 6 ways in which data visualization can help brands improve their customer experiences:

It can make reports more impactful

SaaS brands don’t just sell their products or services and move on — they’re in the business of helping their clients achieve things, and since many of those clients have their own clients to deal with, providing robust reporting functionality is essential for any large-scale software solution (ecommerce and social media tools are obvious examples). Of course, there are always third-party reporting solutions to fill in any gaps.

If you’re running a retail business and you’re trying to make a case for further investment (or trying to negotiate a deal with a potential supplier), which is going to prove more effective: offering up a dry set of your top metrics, or presenting a clear and crisp visualization of your long-term performance? The better a brand can support its clients in creating clear reports, the better it can make its customer experiences.

It can cut through junk metrics

Make no mistake: the more data you collect, the more junk metrics you’ll end up with. The more you stare at static, the more likely you are to start seeing patterns that don’t mean anything, and it’s frustrating when reviewing data to know that much of what you’re seeing is meaningless. Visualization is extremely useful for looking past those junk metrics and finding what matters.

How does this work? Well, in essence, visualization requires you to select primary metrics (or small sets of metrics), forcing you to be selective — and if you use junk metrics, you’ll end up with a comical visualization that starkly shows how useless that view really is. Help your customers cut through the noise and they’ll appreciate it.

It can assist with telling stories

Crafting a narrative with data can be extremely useful, whether you’re pitching to a prospective supplier, trying to raise morale in your team, or attempting to steadily build an impressive brand. Put together a strong case to show that your business is moving in the right direction, and you’ll have people eating out of the palm of your hand.

Of course, data is finicky at the best of times, and it tends to be opaque and confusing at first glance. Visual storytelling is massively more powerful than its textual equivalent, and composing a creative animated infographic can drive a point home with real aplomb. Think of how charities compose social media graphics to give poverty and disease statistics the gravitas they warrant. Offer that kind of functionality to your customers, and you’ll earn plaudits.

It can help show how customer shopping channels interact

Following up on the ecommerce example, retail data doesn’t stem from isolated channels for typical storefronts. Instead, it’s fed through multiple channels — social media platforms, advertising campaigns, SEO traffic, and even offline marketing. Understanding how the numerous distinct elements are interacting is key to improving the overall strategy.

To achieve this, an ecommerce business needs a CMS capable of multi-channel commerce and intelligible visualization, such as Shopify (it happens to have a fairly comprehensive set of best practices), plus every potential traffic source or action tracked and tagged suitably. That data can then be visualized to clarify the wider context and more usefully highlight the most significant areas for improvement.

It can cater to those lacking technical skills

Imagine the first-time software user trying to make their way in a comprehensive suite with more functions and metrics than they can usefully fathom. Trying to figure out how things are going, they stumble into the reporting section and immediately hit a wall upon seeing massive tables of identical-looking data. It’s a tough learning experience.

When dealing with non-technical customers, using data visualization is a great way to ease them into more complicated functions without asking them to handle anything excessively complex. It’s also excellent for providing intuitive controls (more on that next).

It can better suit touchscreen interfaces

These days, we do so much of our online work using mobile devices that old-fashioned menu systems just don’t fit most circumstances. As noted previously, data visualization doesn’t need to be non-interactive: you can use it to devise a fresh control system using drag-and-drop and connective lines to make it clear how different elements fit together.

Imagine someone with limited technical knowledge being able to compose a strong report regardless through using an intuitive touch-based design. That’s what UX can achieve today (as seen on kiosks across the world through companies such as InTouch), and to make it a CX reality, you need only put in the effort to make it happen.

Making data accessible

In the end, this is what data visualization is about: taking complex and tricky data and making it useful and meaningful for any and all audiences. For the 6 established reasons, it can be a powerful tool for improving customer experiences, so don’t overlook it.

The post 6 Ways Data Visualization Can Help Brands Improve Customer Experience appeared first on Reflective Data.