By Laurence Armiger, Sales Director, Zizo
Analytics has come a long way since the late 1970s when corporate data was stored on punch-card databases. In the 1980s and 1990s data was stored on relational databases – a big improvement on the first databases but they were still often unwieldy. And you needed specialist staff to run them.
Now a new use of technology and outsourcing is emerging: analytics as a service. Companies can outsource the hassle and expense of buying hardware and software to analyse the vast amount of data captured from servers, emails, and social media etc. Customers pay a monthly fee for a (typically) cloud-based service which can store data either in the client’s private online cloud or store it in a public cloud (albeit one where data is segmented and protected by meticulous security).
What are the benefits of analytical software services? Well, like other outsourced services, the business saves money on buying often expensive IT kit and software tools for analysing data.
One retailer we know had about 50 people working to improve data from its ERP system on the performance of its business. The ERP system was spitting out reports that were of little value, on tools that were inflexible and hard to change, and to timeframes that were unacceptable to the business, areas that can be amended through an outsourced data analytics/business intelligence service.
Also, the supplier can expand the technology’s capacity at short notice to cope with surges in demand for a business’s product or service. Or reduce capacity when your business is quieter. You only pay for the computing power you use.
Perhaps more interestingly for sales directors and other board directors, Big Data can be analysed much more quickly and effectively. This helps companies lower costs, increase sales and improve customer service. And all for the price of what it would cost to hire a database analyst to only look after your analytical database, without taking into account the cost of other tools & technologies, hardware or training.
There are many use cases and examples of this in action. Sales directors can get updates on their company’s performance every five minutes at the click of button, compared to the month or so it often took previously to go through the normal channels. Managers in retail can see whether a new product is flying of the shelves or gathering dust. Perhaps it’s in the wrong place in the store or maybe sales have spiked in response to a TV advert for the product the evening before.
Companies are also using data analytics services to increase efficiency. One logistics business, for example, did activity-based costing to analyse the profitability of its many contracts. In this example, before using data analytics, the company didn’t have an overview of its contract costs. So many people and processes were involved and the different stages in delivering the parcel, it obscured the costs of each contract.
The company found some contracts were significantly less profitable than it previously thought. Others were more profitable.
By scrapping some contracts and asking more money from other contracts (where it was under-charging customers) the company recouped the cost of the three-month project in one week thanks to data analytics.
It is not just about big data. Small details can make a big difference to a company’s performance. Another retail business analysed its fleet of vehicles using an outsourced data analytics service.
It found that many of its trucks were not turning right during their deliveries, as doing so often involved crossing a busy road which was more hassle and more stressful. Instead the drivers took lengthy de-tours, which increased fuel costs and journey times. The company saved thousands of pounds a month in fuel costs by encouraging its drivers to drive differently.
But analytics can do more. Much more. Until recently most analytics software (and much of IT for that matter) has been used to analyse a company’s performance in the previous year. Just like annual company accounts.
Now, alongside analytics as a service, you may have heard the term ‘predictive analytics’. The idea is that companies use all their data (sales, running costs, customer satisfaction etc. from ERP, CRM and other back-office IT systems) to predict their performance.
A retailer can use predictive analytics to predict if a shirt will sell particularly well during a certain month or even weekend and stock more of them.
New analytics software can also help sales directors make smarter decisions and shape their strategy.
But there is one key component required to make predictive analytics work: human involvement and intuition. It’s not about replacing the sales director or IT staff with clever software.
Analytics alone isn’t able to transform a business or decide its strategy. It’s there to add a string to a sales director’s and company’s bow and underpin their decisions.
Also, in our experience, different workers (from senior to junior staff) often have different expectations about what analytics can do − or whether it’s worth using at all.
Workers may need reassuring about possible return on investment from analytics.
Here are some tips for getting good value from analytics technology:
One, have a good understanding about your business (sales, profits, customer satisfaction etc.) before comparing its performance to rivals.
Two, start your project slowly – for example, focusing on your most valuable customer data first, before widening it to collect and analyse other data. Add new functions to the data analysis every couple of months. The main reason analytics projects fail is because they’re trying to do too much too soon.
Three, share data across your business. Don’t let IT, sales or finance keep their data in departmental silos. Sharing data will help produce ideas on how to do things differently and improve the performance of your business.
Four, measure the return on investment from your analytics after one year. It may take a while to work out any links between improved performance and data analysis.
Analytics is advancing fast, helped by cloud technology and demand from businesses for technology that can help make sense of their vast data and improve their performance. But to harness the full power of analytics, companies need to refute misconceptions of the technology: that it will replace sales, IT staff and other workers, or that it’s 100% accurate on its own. Much also depends on the quality of the data analytics scrutinises.
IT staff shouldn’t have to spend time producing one or two-page reports on the historic performance of a department or the whole business. Far better to keep the lights on and think of ways technology can support expansion into new markets and new products and services. It’s time to give business intelligence back to the business.