Natalie CrampNatalie CrampJune 18, 2020
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10min952

The phrase ‘unprecedented times’ has been used so much in reference to the Coronavirus that it can seem like an empty cliché.

We have after all been presented with new extraordinary circumstances on a daily basis for months on end. Now, with lockdowns easing and the retail industry opening back up, we can be forgiven for thinking the unprecedented times are now at an end. However, this could not be further from the truth.

All of us, and particularly retailers, are entering uncharted waters. Many businesses are moving from the relative clarity of being fully shut or online only to the more complex position of trading under restrictive and unique circumstances. Navigating this new normal is going to be very difficult but it can be made easier by collecting and using data to make more informed decisions.

The biggest question facing retailers is how will consumers react to the easing of lockdown. We saw in the first week of non-essential shops opening up queues around the corner for some outlets.

Undoubtedly, as the weeks go on we will get plenty of news reports indicating whether there has been a complete bounce back, a steady increase, an initial glut followed by a sharp drop off or a worryingly flat response. The reality is that these industry wide stats will take months to give us even a general picture of what is happening. For individual retailers, this will be too late to react and adapt.

Retailers need to take control of the situation by having a proactive strategy underpinned by data. The exact approach will vary markedly between businesses, however, for SMEs right up to large chains, there are overarching principles that I believe will help frame their response:

Identify the data you have

Website analytics, sales data, marketing and social media engagement stats etc. are available to nearly every business and will provide the bedrock for understanding how conditions have changed and (most importantly) could change in the future. If you don’t currently collect this data, it is absolutely critical to set up processes that will store it in a manner in which it can be easily analysed.

Identify the data you don’t have

Often the biggest knowledge gap retailers have is in store. Beyond simple transaction data, retailers are blind to footfall, customer journeys around the store, connecting in store customers with online identities, how and why sales are abandoned and the general sentiment of customers. Add to this incomplete information on online customers and many businesses can be surprised by how little they actually know about their customer base.

This data dearth can manifest itself in low marketing engagement stats, inaccurate predictive modelling for sales and, most visibly, an inability to answer simple questions – for example, ‘which of my customers are vulnerable?’. Recognising what you don’t know is an essential step towards getting the data you need to know.

Define the questions you want to answer

There are dozens of metrics that can be collected and fed into data science algorithms, however, more data isn’t necessarily better. Not all data points are created equal, nor is it practical or wise to attempt to collect everything you can – it is a pointless and time-consuming exercise.

The best approach is to first think about the questions you want and need answers to now. For example, how does in store footfall compare to pre-lockdown? How many customers are not making purchases due to queues because of restrictions? Are the in store customers new or online customers that prefer to now shop in physical locations? From your list of questions, you’ll be able to work with your data specialists to determine the exact data points you need and therefore what new information you need to collect.

Create a plan to collect the data

This is where businesses can often get bogged down in complexity. Dreaming up cunning marketing or engagement strategies to get customers to part from their data may look good on paper, but when time is of the essence and money is tight they are far from practical.

Companies often approach data collection with a near-fatal misconception that customers need to be duped into providing their data. The reality is that many people are happy to provide information directly if they approached in a transparent manner and can see the clear benefit of doing so.

Asking customers questions via in store surveys or online polls is often the most effective strategy. With conditions as they are, most will understand why you would need to know their thoughts and feelings.

For information that can’t be easily obtained by direct questioning or observations from your in store staff (e.g. on footfall), technology can provide assistance. One example is using fintech software that captures and collates card data from purchases, helping you to link in store and online identities.

Analyse the data – quickly

The depth to which a business analyses data is of course dependent on their resources. There will also be large variations in the approach that will work best – updating existing models, creating new algorithms, looking at how data is integrated into the existing enterprise software and Business Intelligence platforms etc..

However, with the retail environment likely to be highly volatile, the key will be to ensure that the insights gained are updated as regularly as possible – ideally in real time. It is also worth noting that getting assistance from data scientists isn’t as expensive or daunting as it sounds.

Often they can work with a business to quickly set up the models that are needed and, with the right data architecture in place, they can, with a little training, be run by any member of staff to continually provide insights.

Ensure your teams understand it

This is worryingly so often missed. There is little point in having a plethora of data or indeed any insight if it cannot be acted upon. This should not be the purview of a data science team but something that every team member across the business is confident to do. They need to understand the insight relevant to their role, its limitations and be able to take quick action – be that switching off a customer marketing campaign, or influencing decisions on ordering from your supply chain.

Test, learn and adapt

Exactly how to ‘act’ will not necessarily be clear – we are after all in uncharted water – however a scientific approach can help to mitigate risk and light the way forward. This is a perfect time to test strategies and retailers need to be bold in doing so, and adapting quickly from the data they get. If, for example, you’re presented with data that in store sales conversions are very low, you can test different solutions to identify which is the most effective. A good data scientist will be able to assist you by stripping out other variables to tell you exactly which factors are moving the numbers.

I know for a lot of businesses data collection and analysis will be far from top of their list of problems to tackle in a post-lockdown world. Nevertheless, I can’t stress enough how important it is to make business decisions based on facts rather than emotion or ‘gut feeling’. Any data that helps to illuminate these unprecedented times may be enough to give your business the edge that helps it not only to survive, but also to thrive.


Lindsay LucasLindsay LucasMay 15, 2020
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9min1626

The telecoms industry is one of the richest in terms of data, but many are struggling to realise its true value and potential.

The very nature of the telecoms industry means that it is a data-rich environment. It, more than almost any other, has a huge amount of regular data streams, often containing valuable data.

Understanding what this data is, where it sits and how it can be used intelligently, in-line with regulations and customer’s expectations, is going to be absolutely key for the industry over the next few years.

Not seeing the wood for the trees

The amount of data collected every day is remarkable. For example, an operator serving 8 million prepaid mobile subscribers generates around 30 million call data records daily, equalling 11 billion records annually. If that operator also provides post-paid and fixed-line services then the data increases exponentially.

Undoubtedly, the potential of this data is huge. Adding to this, payment and contract details, trends in usage, mobile data, insurance, app downloads, Geo locations and on and on, it is easy to see how this data has the potential to add to a company’s knowledge of its own customers and allow it to take data-driven decisions around customer communication and the reduction of churn.

The collection, storage and dissemination of data are in most cases, already built into the everyday operations of most telecoms companies. However, gaining an understanding and getting a grip on the sheer amount of data that telecoms companies handle is one of the major areas of frustration for them.

Growth through acquisition adds to this. The reality is that systems will not have been merged or consolidated so that the data is coming through one system in the same format. So now you have huge amounts of data coming through in potentially different formats in different systems. If identifying the most accurate, useful data was a challenge before, it is a near impossible task now.

What challenges can data help telcos overcome?

The fact that the data telcos hold can add huge value to companies is nothing new. Understanding that data can help with the consistent challenge of customer churn is also widely recognised.

Roughly 75 percent of the 17 to 20 million subscribers signing up with a new wireless carrier every year are coming from another wireless provider and are therefore already churners. This is a huge amount of money and people, so retaining just a small percentage of these customers using data that you already have just makes sense.

Much of this churn is a result of poor customer experience. The latest UK Customer Satisfaction Index from January 2020 is at its lowest levels since 2015. The Telecommunications and Media sector is rated at 74.8 placing it in the bottom four sectors (along with local public services, utilities and transport).

Therefore, improving customer service is absolutely key to reducing churn, keep customers happy and retain that income. The old adage of it costing five times as much to attract a new customer as it does to keep an existing one remains true.

This competitive landscape is not getting any easier. The established telco companies across the sector are being challenged by smaller, more agile, new firms that are able to offer the customer experience expected by consumers immediately. They are not weighed down by legacy systems or poorly integrated infrastructure from acquired companies.

Competitive landscapes have also seen prices squeezed like never before, adding more pressure on telco companies. Margins are minimal which brings its own challenge, but again is one that can be helped by a better understanding of the data that resides within companies.

More telco companies are turning to data maturity assessments in order to find out where data is being held, how it is being used and by who. This full picture is the crucial first step in using the data more intelligently. Without this companies can never have a clear understanding of what data is available to them and how they can use it.

What challenges does data itself present telcos?

The sheer volume of data, as has been discussed, is in itself causing a real issue. It is not just the data collected by the telecoms company itself. Many have to interface with supply chains and suppliers such as BT Openreach. Therefore, the data is coming from multiple places, not just in-house systems.

Alongside this, when data is collected, stored and managed by different business units within the organisation there can be real confusion.

More often than not each business unit is storing and using the data in different formats, most of which cannot communicate with each other. Add to this the third-party data and other systems incorporated via acquisition, the picture is one of data confusion.

The other issue confronting telecom companies is one of an increasingly complex regulatory landscape.

In order to secure adherence, and more importantly, remaining secure, telecoms companies have to ensure that they understand where their data is coming from, how it is used and where it is stored. Without this information, companies are risking laying themselves open to possible breaches.

Overcoming the challenges and making data a key asset by understanding your data maturity

Identifying and understanding your organisations data maturity can be overwhelming.

However, telcos are turning to data maturity models designed to help businesses along the way. Turning to third party solutions takes away the resource pressure of beginning to tackle the huge amount of data held by telecom companies.

The automated process means large amounts of data can be analysed, giving companies an understanding of how their data is collected.

It can gather data across the entire company, from disparate systems and formats. This gives an indication as to where the valuable data is held and how it can be used to allow companies to make informed decisions as to future strategy.

Data undoubtedly continues to cause issues for telecom companies, but the benefits of leveraging it are huge. This has been recognised by many in the industry for years, but now is the time for companies to act.

Data maturity assessments play an important role in taking the first step for a better understanding of the data held within companies. Without this first step, most companies are unable to gain the insight they need to start to address the key issues impacting the sector.

 

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