EXCEL TO EXCELLING: 6 INSPIRING ANALYTICS EXAMPLES

WHAT IT'S ALL ABOUT...


How do you do business? Our research shows that most businesses are still relying on spreadsheets to carry out their reporting, but this comes at the cost of limited insights into the data (AKA gold dust) they produce.

This blog will explore real-life examples of organisations that have moved away from simply inputting data into Excel and are instead using advanced analytics to optimise their business processes and drive value.



Recruiting

Traditional approach:

Successfully recruiting is no easy task and organisations usually have to rely on gut-feel when it comes to deciding on who enters through their doors. On top of this, the fear of a bad hire can often result in a long and pain-staking recruitment process, which drains valuable time and resources.

 

The better approach:

Unilever is taking the all too familiar “guessing game” out of recruiting through harnessing AI to automate and optimise the process.

The FMCG giant created an online platform where candidates could be assessed from the comfort of their own homes. The first stage of assessment involves playing a selection of games to test reasoning, logic, aptitude and risk appetite. AI techniques are then used to assess a candidate’s suitability for the role they’ve applied for, based on the profiles of successful employees.

In the second stage, candidates submit a video interview, which is again examined by algorithms to determine who is likely to be a good fit with the company.

By automating certain decisions in the early stages of recruitment, Unilever has been able to save an impressive 70,000 person-hours and have peace of mind that they’re hiring the right person for the job.


HR Support 

Traditional approach:

Organisations are losing hours every single month dealing with employee requests which often fall on the shoulders of the HR team. This results in HR becoming burdened with time-consuming tasks like searching for company documentation, preventing them from focusing on work that makes a real difference.

 

A better approach:

Royal Bank of Scotland (RBS) wanted to tackle this issue whilst maintaining a digital, yet personal experience for its employees.

They decided to adopt their own internal assistant to instantly deal with common HR queries in the form of a dynamic chatbot called “Ask Archie”.  

Archie was able to respond to questions such as “Where can I find the employee handbook?” “What dates are we closed for around Christmas?” and “Who do I contact about my pay this month?”. It was also able to link to company documents to save time having to manually search for these.

Ask Archie answers around 5,000 queries per month, which equates to almost half of all of the  HR queries the company receives, saving them approximately 1,826 hours per year. They have also experienced a dramatic boost in employee engagement due to employees getting the answers they need faster.  



Customer demand planning

Traditional approach:

Being able to effectively anticipate customer demand is an art and few organisations do this effectively. With a multitude of changing circumstances and realities, organisations must understand how changes right down to a single purchase affect their product demand and inventory levels. The issue is that for most, this is left down to rough estimates based on unreliable data.

 

A better approach:

Dickey’s Barbecue Pit is one example of a relatively low-tech business embracing data and analytics. The American restaurant chain uses analytics to gather data from point-of-sales systems in restaurants, marketing promotions, loyalty programmes and inventory systems to gather feedback on sales.

Based on what the dashboard tells them, they can then take the appropriate action. For example, the system might detect that a restaurant has seen lower than expected sales one lunchtime and that there’s a surplus of ribs. Based on this knowledge, the team can put out a text invite to customers offering them a rib special to help drive sales and use up inventory.

 


Customer service

Traditional approach:

Research shows that among people who contact a brand through social media for customer support, 32% expect a response within 30 minutes and nearly half expect a response in an hour?

Studies also show that for every second a company shortens its call centre handling times, over USD 1 million is saved in annual service costs.

Customers increasingly expect businesses to deliver better customer service faster, but with customer service reps having little anticipation of the nature of incoming calls, customers can face annoyingly long wait times in an attempt to find a solution.

 

A better approach:

Efficiency is at the heart of what Uber stands for, and they took to analytics to improve the handling of their ever-increasing number of support tickets they receive.

To streamline the way they handled customer service queries, they developed a Customer Obsession Ticket Assistant (COTA) – a technology that uses machine learning and natural-language processing to categorise the range of different customer concerns and questions and allocate the most suitable responses accordingly.

This meant that when customers put through a support ticket, agents were able to use these suggestions to solve queries with speed and accuracy, resulting in a decrease of 10% in the resolution time.


Supply chain

Traditional approach:

An effective supply chain is able to provide the right product at the right place at the right time. In a recent study by McKinsey, two-thirds of companies reported that they expected supply chain risk to increase which means that fulfilling this mantra is becoming more and more challenging.

Many organisations find it difficult to sync up all of the different parts of the supply chain, which can lead to notoriously long wait times between stations. In addition to this, lack of real-time insights means that often the service can lack accuracy, with online listings not reflecting stock levels.

 

A better approach:

Lenovo is a good example of an organisation who is leveraging AI to reduce global supply chain cost, complexity and risk.

Thanks to analytics, Lenovo have full visibility into their supply chain which means that if there’s a disruption in one area of the supply chain, they can see the specific orders that will be affected, as well as how this will impact them financially.

These insights have helped them mitigate risk, prevent out of stock disasters, and reduce their average response time to supply chain disruptions from days to minutes—up to 90% faster than before!

Marketing

Traditional approach

When it comes to great marketing, knowing your customer is everything. In this digital world where consumers interact with so many different touchpoints on their journey to purchase, this philosophy has never been truer.

Traditionally marketers have relied on creativity to come up with ideas that captivate their audience, but creativity can only get you so far. Even the greatest ideas can end up producing lacklustre results and this can lead organisations down a path of trial and error with no real insight into what went wrong in the first place.   

 

A better approach

An Abu Dhabi Islamic Bank used analytics to protect their sensitive customer data, whilst gaining a better understanding of their customers than ever before.

Based on their customers’ digital interactions, they are able to get a 360-degree view of their customer preferences right down to the individual level.

This means that they can understand how customers interact and use their products and services across their website, email, SMS and other online channels and then tailor these channels based on their individual preferences.

Overall this worked to create a personal omnichannel customer experience for the bank, significantly increasing their conversion rate to the point that their digital channels are responsible for 20% of all new business that they generate.

 

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Oletta Stewart

Content Writer


Oletta Stewart is a Content Writer for MHR Analytics.

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