Big Data Trends You Cannot Miss Out On!

Big data analytics is on a rising curve with organisations across industries realising the role that it plays in their business growth. The estimated worth of the big data market is all set to witness a substantial increase from US$ 46.34 billion at the end of the year 2018 to US$ 203 billion by 2020. This emphasises on the emergence of big data analytics and the subsequent change in the business landscape.

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Let’s discuss the dominant trends in big data analytics :

Rising demand of big data frameworks

The demand for big data frameworks like Hadoop, NoSQL and Spark is expected to increase by about 32.9% annually. A Forrester Report mentions that approximately 60% enterprises are expected to have Hadoop clusters implemented by the end of 2018. This will accelerate big data processing, improving the accuracy of critical business decisions through real-time insights.

Data visualisation-integrated models

Data visualisation and data discovery will come to the forefront. There has been an apparent shift in the concept of data discovery from simply focussing on data analysis to offering deeper business insights. This will in turn empower enterprises to track and closely monitor visual patterns.

Big data analytics in the clouds

The volumes of data in the database of enterprises have been increasing in leaps and bounds. Despite constant efforts from data scientists to break down. Analysing such vast volumes of information has remained a growing challenge. With lack of sufficient manpower to make the best use of these data, it is time to upgrade and digitise their software systems to the clouds to simplify big data processing. This will help in interpreting the unexploited data, also referred to as ‘dark data’.

Streaming Analytics is transforming Big Data Analytics

While cloud software has been gaining popularity since quite a few years. Streaming analytics takes it one step forward by analysing data while it is still being created. This phenomenal big data analytics trend is re-defining the meaning of real-time data analytics. This also eliminates any scope of revisiting or replicating datasets, ensuring zero tolerance for delays in big data processing.

Evidently, the future lies with big data analytics. However, the big question is “What next?”. It will definitely be intriguing to wait and watch how big data analytics unfolds in the coming years and the dynamic role that it will play in interpreting extensive volumes of data.

Applications of AI in Retail

Application of AI driven process in retail will not only help retailers acquire new customers, but also boost repeat business. Increased accuracy in personalized communication to the customers along with tailored recommendations and offers will compel shoppers to strengthen their loyalty as they will begin to associate the retail brand with personalized, relevant experiences. Having realized this, retailers are looking to invest and use AI heavily in this coming year

According to a research, global spending on Artificial Intelligence (AI) in retail is expected to grow nearly fourfold over the next four years, from $2 billion in 2018 to more than $7 billion in 2022. This is expected to happen as retailers look at new avenues to boost their efforts to offer personalized customer experiences. As such, retailers will heavily invest in AI tools that will allow them to differentiate and improve the services they offer to their customers. These tools, ranging from automated marketing platforms that generate tailored and timely offers, to chat-bots or voice assistants that help to provide instant customer service, will be in the radar of retail brands.

Progress in AI and machine learning in the recent years gone by and those to come will be exponential. The combination of AI, cloud, Big Data have already begun the transformation of the retail industry and this will reach new levels in the near future. As AI leverages big data to personalize experiences, retail companies are looking at these applications to garner robust competitive advantages. As per the report, retailers’ spend will be the strongest in the customer service and sentiment analytics area to understand customers’ reactions to the products purchased and the service received, all being possible with the application of AI in analytics. This will prove to be the breakthrough for retailers looking to improve their customer experience.

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AI will also be able to help in actually predicting the purchasing behaviour as well as the needs of in-store customers. This means that sales staff can have this kind of information handy and will therefore have an idea of what a customer is looking to buy before they even ask for help. This will be a huge step in predicting the customers’ needs in advance and being able to serve them aptly thus revolutionizing customer service at the store.

AI driven insights would also be leveraged to not only design new product ranges but also to plan and create marketing and promotional campaigns and offers. Optimizing product pricing and discounting with the help of AI will prove to be beneficial to retailers. Further AI-backed demand forecasting is also increasingly becoming an essential tool for retailers. Understanding customer demand and accurately planning and managing inventory has become critical, especially during mega shopping events such as Black Friday, Cyber Monday, Singles Day and Chinese New Year. Demand forecasting with the help of AI will definitely empower retailers to be prepared for such big events in advance.

Mentioned above are some of the applications of Artificial Intelligence in the retail business and as time progresses, these applications may get further enhanced and there may be new ones that retailers may want to use and fuse into their businesses to further enhance their customer experience across channels.

5 Questions – Before Investing In Retail Business Intelligence (BI)

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Big data eliminates the uncertainty out of the enterprise, provided the right software and tools are used to break it down into accessible bits of information. When deduced and used correctly, it helps retailers identify the pain and gain areas of business. It also provides actionable insights into customer behaviors, demographics, brand affinity and the ability to create targeted campaigns.

But will Big Data give you the right data? Ask the following before investing in retail BI solutions.

Who is your customer and how effectively can you reach out to him?
While Big Data can help you be more strategic in customer engagement, it is necessary to ascertain who your audience is and how will you reach out to them. Once you have understood this, you can allot the necessary KPIs to the data project and establish the foundation of success.

Does it provide the crucial 80/20 analytics?
Retailers and marketing teams understand the value of deriving the 80/20 analytics. On average, 20% of your customer generate 80% of your top-line revenue. So, while you might be able to acquire information on thousands of customers, it is more important to know your top customers. Understanding their traits and ticks would potentially surge revenue and recommendations both.

Do I have the employee strength to support it?
The influence of emergent technology permeates all industries. There is often high pressure and anxiety related to ‘Big Data’ adoption, as a business process. In the eagerness to obtain the latest technology software and application, retailers tend to miss the long-term requirements of the system. . The BI application phases are to be supported by people, within the organization, with the right skill set to derive value from the vast enterprise data and validate system results.

Do I have the company culture to sustain it?
Big Data technology wielded as a demonstration of competitive advantage will only take you so far. Deep and comprehensive planning is essential to understand the levels of analytics needed by the current and projected business scope.

Is it social?
Social media integration with the BI system is crucial to not just accumulate, but also to validate CRM data. It helps generate a community-based correlation and engagement with customers. Both business functions, BI and social media, feed information to each other. This helps you reach out to a bigger circle of potential prospects with targeted campaigns and communication.

Top 5 Questions – Before Investing In Merchandise Planning Technology

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The average consumer has changed. Retail has changed to keep up with consumer expectations. And, new technologies are continually evolving to change buying behaviours all over again. The only element that remains constant in the cycle of change is executing a customer-centric, interactive and personable brand experience. Effective merchandise planning helps you deliver just that!

Can it provide an omni-channel presence?
A customer expects seamless and consistent brand experience in-store, online, through mobile apps and social media. To remain accessible to the customer on multiple channels requires omni-channel planning and forecasting. Products, promotions and pricing has to be consistent across all retail channels – reinforcing customer trust, purchase frequency and loyalty.

Can it crunch Big Data?
Consolidated enterprise data in the form of Big Data is run through a common analytic engine to gain actionable customer insights and understand purchasing behaviour. Merchandise analytics can be used to ascertain trends and future market potential in different seasons, occasions and allow intuitive management, reducing lead times.

Is it working with real-time information?
Retailers need to provide on-trend, timely and precisely-priced merchandise as and when the customer demands it. Accurate demand planning in a highly competitive marketplace is key to optimize product lifecycle and profitability. Real-time visibility and control of business operations helps make quick strategic procurement, promotions and placement of merchandise.

Is it scalable?
When business is changing and growth is happening around the world, around the clock, your software systems and processes need to be agile. Merchandise planning, in an expanding enterprise, needs to be as cohesive as it is comprehensive. Flexibility in the system architecture leads to optimized assortments and optimal merchandise cycles.

Does it provide stable automation?
The most elemental and important feature is the stability of the merchandise planning process. All functions mentioned above can only be productive when the system platforms are aligned and stably connected to enterprise operations. It can drive superiority in the supply chain and competitive advantage in customer demand.

Making A Dent In The Retail Universe

ETP blog Retail disruption

Disruptive innovation, a term of art coined by Clayton Christensen, is described as an innovation that helps create a new market and value network, and eventually disrupts an existing market and value network (over a few years or decades), displacing an earlier technology.  The retail business is experiencing tumultuous change and instead of fortifying a singular approach, retailers are adopting multi-dimensional technology through disruptive innovations which are fast establishing themselves within the industry. Emerging retail technology like Mobility, Digital Wallets, Big Data, Geo-fencing, etc. has helped retailers explore and expand to a global market. Simultaneously, it is providing the right system fitments that help reduce costs through optimized operations, gain profitable growth with better business intelligence and increase brand equity which becomes proportional to the rising customer connect and satisfaction.

The best example to support the above statement would be the continuing ascent that Apple has charted and secured for the future. In 2014, Apple retained its position as the world’s most valuable brand, increasing its brand value by 21% to $118.9 billion. The company trail blazed an era of disruption starting with mobile technology in 2007 and powering on with business apps and devices like iPod and iPad which fast became trendsetters in device technology.

Mobility in retail is changing the way customers interact with brands and consume products, services and information. Most retailers have gained and sustained increased productivity enjoying the ‘first mover advantage’ with mobile internet. The internet of things has made it possible for retailers to build assured ROI within the business. These are not device centric innovations but rather a universal solution that realizes projected growth and fulfills customer experience and the brand’s USP. Adding to the same, digital wallet technology is quickly catching on to become one of the key payment modes, especially in industries like retail, banking, entertainment, etc. Forrester research data reveals that 50% of US smartphone owners are open to using their mobile phones for in-store payments. While this technology lends access to finance and payment related functions on handheld or desktop devices, it also manages a behemoth of value added services like offers, discounts, loyalty credits, etc. and even one-click comparison shopping.

Big Data technology has changed retail market dynamics for good. Retailers are now targeting individuals rather than demographical sub-sets, building relationships and awareness over ‘sales pitches’. Recently, Walmart launched the social, mobile and retail-focused Walmart Labs in Silicon Valley, and it acquired a handful of tech startups, including Kosmix and Vudu. Walmart Labs developed the search engine, Polaris, which uses semantic search algorithms to understand what someone is searching for and thus boost sales. On top of that, the lab’s Social Genome Product culls through millions of tweets, Facebook messages, blog postings, YouTube videos and more to detect purchase intent and drive ecommerce.

Geo-fencing is the software application which uses Global Positioning System (GPS) or Radio Frequency Identification (RFID) to create a virtual geographic fence. This can be dynamically defined and tracked as per requirement. Although this technology has multi-dimensional uses e.g. hospitals, human resources, high security, etc., it is being quickly adopted in retail to creatively engage and inform customers – attract them to the store at the most opportune time when they are within the location parameters. Retailers can also use the registered and opt-in customer data to further provide relevant content, as per their previous buying or payment history. For example, the Walgreens app has adopted Apple’s Passbook feature which uses geo-fencing to remind users to refill or pick up their prescriptions when they enter a Walgreens store.

The above examples represent a snippet of how disruptive technologies are making it possible for retailers to infuse advantageous sophistication into their overall brand experience. It represents profound opportunities, especially considering the potential of varied fast emerging retail markets worldwide.