Analytics & Data

Data has long been one of the key talking points in retail innovation with the promise of revolutionizing operations and improving customer engagement. Technology is now up to the task. The time for talking is over.

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How you can use the RetailTech Model

The first stage is Research & Development, when an innovation is not fully-fledged and has not yet been adopted beyond prototypes, trials or POCs.

New technologies typically go through 5+ years of R&D, though the timeframe will vary substantially depending on the degree of innovation entailed.

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Model Stages

The Leading Edge stage indicates when an innovation has moved out of R&D and into operation. Approximately 5% of the market adopts the innovation at this stage, usually start-ups and a few industry players known for being forward-looking.

Sometimes, an innovation is picked up from another sector. As indicated in the timeline below, it typically takes 1 to 3 years to move from the Leading Edge to Early Adopters stage.

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Model Timeline

At this stage organisations are more risk averse than those at the Leading Edge, but are still keen to be in the industry’s upper quartile and adopt a new technology.

The broad timeline for technologies to remain at this stage is 2 to 5 years at which point they will have reached around 25% market adoption.

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Model Origins

By this point a technology or business innovation can be considered as Mainstream since it will have been implemented by around 50% of the market.

2-5 years is the typical timeframe for this stage.

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Model Lenses

Technologies in the Late Adopters stage have been widely adopted across the industry with 80% - 100% of the market using them after a further 5+ years.

Not all technologies end up being adopted by everyone, with some 20% of technologies never reaching full adoption.

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R&D

The first stage is Research & Development, when an innovation is not fully-fledged and has not yet been adopted beyond prototypes, trials or POCs.

New technologies typically go through 5+ years of R&D, though the timeframe will vary substantially depending on the degree of innovation entailed.

. .
5+
Leading Edge

The Leading Edge stage indicates when an innovation has moved out of R&D and into operation. Approximately 5% of the market adopts the innovation at this stage, usually start-ups and a few industry players known for being forward-looking.

Sometimes, an innovation is picked up from another sector. As indicated in the timeline below, it typically takes 1 to 3 years to move from the Leading Edge to Early Adopters stage.

. .
5%
1-3
Early Adopters

At this stage organisations are more risk averse than those at the Leading Edge, but are still keen to be in the industry’s upper quartile and adopt a new technology.

The broad timeline for technologies to remain at this stage is 2 to 5 years at which point they will have reached around 25% market adoption.

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25%
2-5
Mainstream

By this point a technology or business innovation can be considered as Mainstream since it will have been implemented by around 50% of the market.

2-5 years is the typical timeframe for this stage.

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50%
2-5
Late Adopters

Technologies in the Late Adopters stage have been widely adopted across the industry with 80% - 100% of the market using them after a further 5+ years.

Not all technologies end up being adopted by everyone, with some 20% of technologies never reaching full adoption.

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80%-100%
5+

The first stage is Research & Development, when an innovation is not fully-fledged and has not yet been adopted beyond prototypes, trials or POCs.

New technologies typically go through 5+ years of R&D, though the timeframe will vary substantially depending on the degree of innovation entailed.

The Leading Edge stage indicates when an innovation has moved out of R&D and into operation. Approximately 5% of the market adopts the innovation at this stage, usually start-ups and a few industry players known for being forward-looking.

Sometimes, an innovation is picked up from another sector. As indicated in the timeline below, it typically takes 1 to 3 years to move from the Leading Edge to Early Adopters stage.

At this stage organisations are more risk averse than those at the Leading Edge, but are still keen to be in the industry’s upper quartile and adopt a new technology.

The broad timeline for technologies to remain at this stage is 2 to 5 years at which point they will have reached around 25% market adoption.

By this point a technology or business innovation can be considered as Mainstream since it will have been implemented by around 50% of the market.

2-5 years is the typical timeframe for this stage.

Technologies in the Late Adopters stage have been widely adopted across the industry with 80% - 100% of the market using them after a further 5+ years.

Not all technologies end up being adopted by everyone, with some 20% of technologies never reaching full adoption.

Pivot on the Customer, Not the Organization

As data sources expands, and the opportunity to derive meaningful insight through analytics increases, organizations find themselves able to focus on individual consumers, not just the broad customer base or demographic segments. In this way, it’s becoming easier and more affordable to understand demand for new products and services.

No longer do retailers need COTS products to deal with data. OpenStack and HADOOP make it possible to see through the deluge, integrate and analyze data. Using “schemaless” databases, early adopters are uploading structured and unstructured data. This approach enables them to split information into reports, archives and “hot” data, a close-to-real-time information source that provides an entry point into the customer journey.

The opportunities expand with the Internet of Things and connected devices. Information can now be gathered everywhere from the shop floor to the loading bay. Sensors can be affixed to trolleys and baskets, with data sent over the store’s Wi-Fi. Coupled with mobile applications, this information offers enormous opportunities for monetization and personalized customer offers and interactions.

Data from in the store can better inform decisions about hotspots, aisle-ends and shelf edges, improving negotiations with suppliers around promotional monies and advertising funds. Additionally, IoT-delivered data can boost productivity for store associates, evaluating where employees are and how they engage with shoppers.

Explore the RetailTech Model for Analytics & Data to understand these and other technology innovations changing the retail experience. 

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Analytics & Data Expert

Alan Smalley
DXC Analytics & Data Management Practice

Arrange a meeting with Alan Smalley

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