Five trends that will shape the retail market in 2018
As technology continues to shape the consumer retail experience, organisations are learning more and more about how these new devices and experiences affect customer behaviour. Success will be determined by data - how it is collected, interpreted, and acted upon.
From budding startups to global enterprises, this growing commodity is triggering retail organisations to deploy business intelligence (BI) solutions that will elevate and accelerate data-driven decisions. Successful organisations are prioritising a modern BI approach, and in turn, priming their workforce to be the most analytically savvy generation ever seen. For a competitive edge in 2018, organisations must recognise the strategies, technologies, and business roles that can enhance their approach to BI.
Here are five analytics trends that we’ll see in the retail and consumer-goods markets this year:
- Retailer/Supplier relationships are strengthened by visual analytics
Consumer orders are becoming more and more complex; in-store, online, or a combination of the two. In response, retailers have shifted their focus to cloud-based SaaS platforms that automate the sharing of information (using visual analytics) to better align with suppliers and handle this growing web of complex orders. These new-shared analyses—based on granular, transaction-detail data—allow for strategy validation and better coordination of fulfilment times to meet a growing complexity of ordering scenarios including in-store, online and order online with in-store pick up. This is leading to higher on-shelf availability rates and fresher product offerings, which create higher levels of customer satisfaction.
An example of this can be seen with Japanese home centre retailer, GooDay, who worked with their top 50 vendors to create inventory dashboards, shared in the cloud through a SaaS business intelligence platform. This allowed the retailer to shift towards a just-in-time inventory model, reducing inventory levels by 50% while increasing sales and in-stock position, freeing up working capital.
- Natural language generation (NLG) supplements visual analytics at scale
Natural language generation (NLG) interaction via popular voice-controlled digital assistants, like Amazon Alexa and Google Home, are augmenting user experiences with how we interact with computers. Voice query with visual analytics will expand in 2018, but we will see natural language narratives merge with visual analysis.
In practice, emerging NLP technologies will bring context to data, allowing anyone to ask questions and increasing data accessibility for everyone – from executives all the way down to front-line workers. This will allow for faster adoption from a change management perspective, educating employees on visual analyses without formal training and helping users obtain deeper insights.
We recently teamed with Censuswide to ask UK retail decision makers what technologies they are using and plan to use in 2018 - 39% of those surveyed state that they are using NLG in their business, while 30% state they intend to use it next year. As people interact with their real-time data using simple touch technology, narratives are instantly generated through sophisticated APIs (application programming interfaces), depending on the context of the analysis. Executives down to front line workers benefit greatly from this combination.
- Augmented Reality (AR) conversion metrics will be the hot new retail KPI
With augmented reality (AR) software baked into the two leading mobile operating systems (Apple with ARKit and Google with ARCore), AR will now become the new norm. This software will pave the way for retailers to engage with customers in new and exciting ways and AR will allow retailers to display information about products for their customers that is not just limited to static information, but dynamic content such as videos, characters, location based information and 3D models. An example of this happening now comes from Mountain Dew. The company has recently partnered with hit television franchise “The Walking Dead” for a “zombie-fied” AR experience app.
In the near future, customers will be able to look through their phone lens down a crowded street or mall and see queue times, how busy a store is, or better yet, see deals pop up for products that are relevant to them. Customers will also use AR in their homes for large items such as furniture and fixtures to see how the product would look in their space before purchase. In-store, customers will scan a product to see customer reviews or even get health information for food products or quick food prep instructions.
This offers a big opportunity for retailers and consumer goods brands to learn new insights about their customer base. Tracking AR conversion metrics as a key performance indicator will reveal the type of AR content that resonates with customers, how long they engage and what contributes to a successful purchase path. Brands can also measure how AR helps to reduce returns for large, costly items such as furniture, appliances, and fixtures.
- AI and machine learning will become table stakes for retailers
Machine learning (ML) will enable marketers, category managers and merchandisers to more accurately predict which promotions and discounts are needed, without adversely impacting profitability. Algorithms combine the huge amount of historical and real-time data to automatically predict sales during periods of promotion, continuously “learning” as more data and variables are ingested and ultimately providing better results.
ML is also changing the way we pay for products. Amazon, who is about to open up its Amazon Go store, is using a combination of mobile apps, sensors in the store and on the shelf computer vision technology and ML to give customers a frictionless checkout experience. Customers log into their Amazon Go app, walk into the store and walk out with a product without actually going through a check out process. No lines, no checkout.
This trend is shifting the market standard for the omnichannel customer experience, with personalised website content, product recommendations, price optimisation, supply chain optimisation, and laser-focused ad targeting.
- Lines between applications and analytics will continue to blur, helping knowledge workers
One of the biggest challenges for employees within retail organisations is having to chop and change between different browser windows and applications while completing critical tasks. To combat this, visual analytics is being embedded within applications, and vice versa. This trend allows employees to stay in the flow of their work, seamlessly using analytics to take action within an application.
For example, retailers are sharing top selling products with vendors by embedding dashboards into existing applications, allowing vendors to conduct visual analysis and interact with data directly. This means that vendors can seamlessly interface with inventory management web applications to understand what products are selling best and assess stock levels in real-time.
Another example involves retailers who share product sales velocity to their vendors along with current inventory quantities. Vendors will conduct visual analysis to interact with the data, and now, they’ll be able to seamlessly interface with the inventory management web application to update inventory in real-time. As the vendor updates current inventory levels, the visualisation will automatically update based on the new real-time data.
This blurring of application and analytics will allow faster workflows and end users and ultimately better business decisions.
In due course, these technologies will enable retailers to become more competitive in 2018, but to take advantage of these trends and to derive the most value from them, retailers need to consider the ‘data deluge’ this new technology will usher in. And, importantly, retailers need to establish how they will analyse, interpret and understand this data or it will be a missed opportunity.