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The failure to manage cyber risks will disrupt digital business in the current era and expose organization to possible impacts beyond opportunity loss. The degree to which CIOs involve in digital risk management will be a critical factor to circumvent such perils.
Digital advancements and change in the technological paradigm such as cloud, IoT and mobility have made cyber security an absolute necessity to safeguard enterprises from ransom ware.
The problem in front of CIOs is not only unregulated IoT devices in the enterprise , but also the nature of the devices themselves. Security needs to be improved in the design process and is the top strategic pillar of priority.
In the face of increasing cyber-attacks and more multifaceted, stringent data privacy laws, security has become a priority discussion in the boardrooms of organisations across different industries.
In this blog, I would like to explore the key drivers to implement a cyber security strategy and some of the preventive measures in case of threat to business. It also illustrates some latest information on cyber security solutions and the organizations response to dealing with the cyber security skills gap. It also analyses on how CIO’s are handling and prioritizing the changing cyber-security landscape.
As CIOs decide on risk levels they’re equipped to accept and pursue their security objectives, as information/data becomes critical for businesses.
Executive engagement towards cyber security
Cyber security accountability must lie with the CIO, but the culture of security needs to be adopted by the whole enterprise. Principal causes of cyber security occurrences result from employee negligence. CIO’s efforts endure to flounder against the number and variations of different cyber-attacks which keeps increasing continuously.
To combat and recognize these threats effectually, CIOs and IT executives need to cement an effective IT security strategy that enables the right tools and technologies at the same time foster a culture of security.
Several mechanisms together with a charter, policy, strategy and governance mechanisms form a digital cybersecurity program that delivers the suppleness required to enable business plans, notify risk trade-offs and respond to ever-changing threat environments.
There are no prescriptive approach organizations that give comprehensive assurance that all rational steps have been implemented. CIO’s plays the imperative role for setting direction for the organization to evaluate their own situations and assess a number of factors to make an informed judgment according to different scenarios.
The CIO becomes the key anchor emphasizing the linkage between business and cyber risk. This needs to be accomplished across, technical, non-technical staff, with the influence from the board. This is a critical time for CIOs to be thoughtful in their implementation and communication framework of cyber risk management issues across the stakeholders in the business. Prioritizing organization’s restrained business design and environmental factors, the CIO will be in a position to cover external threats and regulatory requirements
CIOs can’t shield the organizations on all type of risk and is practically not viable. It is imperative to create a sense of balance between sustainable set of controls to protect their businesses with their need to run them. Taking a risk-based method will be a critical point to establish target levels of cybersecurity readiness. Budgeting alone does not create an environment for improved risk posture, CIOs must prioritize security investments to ensure that there is a true value for budget assigned on the right things this needs to be based on business outcomes.
Attacks and compromise are inevitable, and, by 2020, 60% of security budgets will be in support of detection and response capabilities.” — Paul Proctor, Gartner vice president and distinguished analyst
Cyber Security Sequence CIO’s could consider:
Consider a robust Risk-Based Method to Improve Business Outcomes: Cybersecurity issue requires judicious risk management that can be done effectively. This approach should be measurable and most importantly enable decision making and executive engagement.
Establish Cybersecurity and Risk Governance to enhance Information Security:
Effective governance is a cornerstone of security programs, CIO should ensure there is right leadership for risk management to support and implement governance and mitigate the risks for assurance.
CIOs Should Mitigate Cybersecurity risk have aligned to the Lens of Business Value:
Postulates that CIOs should address cybersecurity challenges like a business function. This will enable them to bring levels of protection that support business outcomes in accordance with the business value.
Cybersecurity is complex, it requires a specifically designed program that enables resilience, agility and accountability
Organizations that rely on obsolete, basic approaches towards security program management will continue to experience incompetence and internal disconnects. This will reflect in failure to deliver optimum business results. Organizations that roadmap more complex, but agile approach will position themselves for digital business success and resilience.
The cyber threat landscape continues to evolve with significant attacks happening, especially over the last decade. The changing paradigm of businesses in adopting IoT has a surge in these attacks. Greater amounts of threats coming into that space has a direct relation to consumer related devices, in the form of machine to machine traffic for businesses.
A CIO has an imperative role to instate security across the organization and business lines. The responsibility extends for effectively handling risk mitigation that span the spectrum across the entire organization. This needs a laser focused approach that is ingrained into the daily operations of the IT setup but as well for the enterprise, products they deliver in the form of digital services.
The CIO’s role in security makes them suitable by the fact that they understand the consequences of technology. As enterprises endure digital transformation, CIO’s recognize that a lot of value comes in the information and delivery of those digital assets. The CIO is equipped with top notch expertise within the organization to comprehend different risk scenarios and successfully implement it across multiple cross-functional areas.
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Artificial intelligence is fast making its way into mainstream education. I do not infer as part of the standard technical curriculum. But several schools, colleges, universities and other academic institutions are adopting AI in the process of delivering impactful education to students and their numbers are rapidly increasing.
Across the world, we are seeing AI augmentation in different facets of the education system – from automating routine tasks that teachers have to perform to crafting personalised education curriculum that is line with a student’s aptitude and areas of interest.
The education sector in India suffers from deep-rooted challenges that need wholesale solutions. The bulk of our students is compelled to go through archaic pedagogical methods that are employed to deliver static and outdated curricula.
For a while now, Bill Gates and other tech stalwarts have been excited by the idea of infusing AI into the education system. Bill Gates calls this bouquet of technology-driven, impactful delivery of coursework as ‘Artificially Intelligent Tutoring Systems’ and hopes that it leads to better internalisation of course content. This column shares some of the areas where AI can leave its mark on the education system and revolutionise the way the next generation of students learn.
Freeing up Teacher’s Time
Teachers are burdened with several menial, low-value tasks that are ripe for an AI augmentation. These tasks neither deliver better learning outcomes nor improve student experience. The time our teachers spend performing hygiene activities – from taking the attendance of the class, evaluating and grading tests and assignments and performing peer reviews – is enormously wasteful.
The time spent by teachers can be easily unlocked through AI, helping them focus on what they do best – teaching and coaching for success. Bringing in AI into the core way-of-working of schools today will help eliminate these burdensome tasks in the following ways:
• By curating tests for students automatically based on the aptitude of students in the classroom. Rather than relying on teachers to conjure up questions in the classroom, AI can help tutors assess the learning level of students and contextually bring up questions. Teachers will be able to administer tests much more easily by using a gradational question bank powered by AI
• Grading the administered tests and assignments. This is another time-consuming and often low-value task that can easily be taken up by AI administered-tests. AI can help automate the repetitive task of grading tests, thus helping teachers focus more on how they can create a better platform for learning by coaching and solving questions from students. AI-graded tests can also help bring up commonly occurring patterns of errors (ie, are students mainly making the same mistakes?), in effect providing input to teachers on which lesson plans require more impetus in the next class
• Ease out repetitive administrative tasks. Teachers also spend hours over the year submitting periodic reviews to their supervisors and coordinators, taking attendance and peer reviewing the efficacy of other teachers. This workload can also be supported by AI – by maintaining automated attendance logs, summarising the test scores of students and reporting the performance of teachers
Curricula, Content Planning
The present-day curricula delivery process is largely inefficient. The current paradigm requires a teacher to deliver pre-designed, standardised content to a classroom full of students with diverse aptitudes and interest levels. The negative impact of current pedagogical methods can still be manifested through the employability score of the current generation.
By leveraging the variegated applications powered by AI techniques, academia will not only be able to deliver more personalised curricula and lesson plans but also improve students’ understanding and retention of the coursework, leading to an improvement in educational outcomes. Here are a few examples of how we can enable those:
• AI can be instrumental in creating a culture of continuous improvement among teachers. By tracking their performance across different key metrics, the educational system will be able to uncover the areas where teachers need support and coaching more effectively. AI can also help curate the coursework for teacher improvement, thus making sure that teachers are continuously updated and can continuously refine their craft
• By infusing AI into the skills and aptitude assessment process for students, schools will be able to better judge the current level of understanding among students for a particular subject area as well as where their innate inclinations lie. Often, students are unclear or unsure about how they can make the most of their talents and how they can channel them into a trade. AI can help schools map out the data of previous students, their career achievements and tie that back to educational research. This will allow schools to accurately predict the subjects for which a student has a natural inclination and then coach her in that direction
• AI can also use data around student attention, interest combined with their aptitudes and abilities to recommend customised coursework. This will help students build a structured career path. This AI-centric approach will foster personalised training pathways and provide students with the skills needed to succeed in their future professions, rather than burdening them and staggering their confidence as the current system does
Optimising Classroom Experience
To fully unleash the creativity and expertise of teachers, the education system needs to also imbue AI-led applications in the classroom on a day-to-day basis. This will enable teachers to work at full throttle. Time spent on minding students and reorienting classroom methods to ensure better student engagement can be saved by using AI in the following ways:
• AI can help improve the tracking of students’ attention levels and help teachers intervene before a student loses interest in the classroom content. While teachers are conversant in minding students that actively disrupt the classroom, engaging students who are quietly inattentive is a comparatively difficult task. By employing attention tracking devices, teachers can much easily monitor the attentiveness of the class and mind them before they tune out
• By aggregating the attention scores of the classroom, AI can help teachers devise a more potent mix of teaching, testing and activities – to continuously ensure better class performance and engagement
AI can bring a plethora of benefits to the education system at large, providing improved educational outcomes to all stakeholders – students, teachers and parents. Through personalised curricula, improved efficiency in the time management for teachers and effective in-class monitoring and assistance, AI can shift the paradigm of how the education system works and how coursework is consumed and leveraged by the next generation of students.
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The Internet of Things(IoT) has profusely pertinent applications. The effectiveness can be realized through operation and integration of the IoT across applications from domestic use to large scale industrial usage.
In this blog write-up, I would like us to deeply examine dynamics of IoT for SME’s to discover a range of applications and advantages to potentially become a driving force by looking at some of the critical unsolved problems. IoT potential is looked through multiple lenses in this sector, particularly its implications and application across SME landscape.
It is imperative to understand that analytics and IoT are two sides of the same coin. Basically, the information gathered from sensors requires analytics applications. The duo combo will influence to make informed decisions based on the data and behaviors collected.
From this perspective, application and its use have been extended for large industries and organizations. One of the potential benefits that IoT offers is in saving of costs related to process automation and rebound in customer satisfaction.
Similar benefits of IoT can be translated and enabled for SMEs in a relevant scale. SME’s have started embracing IoT and its related technologies. Here we would try to demystify IoT for SMEs such as retailers and companies with more moderate capitals by examining the developing role that it could play in both the immediate and long-term future. Smart devices and sensors are vital for this Machine-to-Machine (M2M) link. By applying machine learning to exploring how IoT could be used to transform businesses, we will envision ways to apply and adopt to SME related challenges
IoT in Retail
Retail across business are a strategic fit for IoT characteristics and intelligent sensors that can measure them. Some areas gaining pace in the industry include Automated Checkouts, Personalized Discounts, Beacons, Smart Shelves, In-store Layout Optimization and Optimizing Supply Chain Management
Sensors acts as the gate way for the above-mentioned areas and are placed at strategic points to capture customers interests, popular and moving brands, kinds of customers etc.
This information will enable customer segmentation and create applications designed for each segment such as promotions or discounts especially during launch offers for new products. In conclusion, IoT for SMEs can enable businesses to design enhanced strategies based on captures information through sensors.
Managing warehouses and production lines
Another potential segment that offers a strong application in IoT for SMEs is warehouse management. Sensors enable to track movement of goods in the warehouse or production lines. They also calculate the count of inventory creating a automated systems to create flags when the merchandise/raw material are running short. Stock replacement/ replenishment requirements can be triggered automatically with alarms.
IoT in supply chain management
Service delivery is another prodigious application of IoT for SMEs. Again, sensors play a critical role in enabling the status of shipment or delivery at every stage. Apart from the above, it is significantly used for calculating improved trajectories for final mile delivery times.
Optimal routes for the delivery to improve the overall customer experience at minimal cost is key application of IoT in this segment.
Predictive and precautionary maintenance
Another application where IoT for SMEs is gaining rapid pace and is highly attractive is predictive and preventive maintenance. Here it enables a system for alerts for early detection and timely replacement of parts or status updates of machines for remote management.
End to end (E2E) operational application of IoT
Intelligent operations begin with integration of data from manufacturing, distribution and sales & marketing divisions. The factual application and advancement of IoT is to integrate all this data in creating new e2e products and services based on preferences of customers in combination to the data collected and validated.
IoT enabled healthcare
Healthcare services and clinics offers personalized service to accompany patients beyond the visits. IoT for SMEs provides solutions for these clinic-based models that result in a competitive advantage. This enables to redesign the dynamics with patients a simple example could be to prompt a trigger for the ophthalmologic patient to replace glasses or improvement tracking. Use of sensors and Big Data also can give the complete vision of an operation and relevant tracking.
Customer based business models
IoT also offers an opportunity for a personalized service for an SME dedicated to plumbing and its predictive maintenance of spare parts to predict pipe installation failure by reviewing its surrounding conditions via application from your cell phone
The above mentioned are some of the many applications and advantages of enabling IoT for SMEs. The principle remains the same exploiting cutting edge technology allows to improve business model through informed decisions based on the data that IoT provides.
Security is a very important part of the above implementation. All technology is vulnerable to attacks. It is critical for the SMEs to consider security as part of the implementation. Below section illustrates some of the guidelines.
Digital Security for SMEs
Security is an essential aspect for SMEs or large organizations. IoT are also highly vulnerable to such attacks. It is important to factor every aspect of your IT architecture with the right security programs. This is key for deployment and commissioning of your sensors and Big Data programs to secure data.
SMEs need to consider aspects of hardware and software associated with IoT implementation model. Emphasis need to be laid on the types of networks, communications and back-up etc. and take inventory of equipment’s, software’s for the type of security that will protect attacks from identified vulnerabilities.
In summary, SME’s are becoming more digital to deliver a connected and seamless experience, IoT will trend among the latest technologies. The emergence of this technologies give rise to newer job roles such as IoT Managers, IoT Business Designers, full stack developers etc. in this sector. The functional and technical areas of these roles span across the expertise of applying sensors, embedded devices, software and other electronics to businesses with front-end and back-end technologies.
The rise of exponential technologies such as IoT and the need to stay upbeat with it, allows scope for the changing landscape of SME’s through new opportunities and roles. IoT will continue to be in the fore front of this changing landscape for SMEs while it is imperative for them directly boost this digital frontier.
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As AI continues to dominate discussions amongst the CXO’s of Fortune 500 companies; Startups might in fact be at the pole position to derive strategic gains accruing from leveraging AI. Armed with accessible funding, young and upbeat talent professionals and overall buoyancy in the demand consumption, Startups are increasingly challenging and upending incumbent businesses. This is attributed to a substantial extent due to their unwavering focus on adopting exponential – including artificial intelligence(AI) – to acquire, retain customers, embed AI across the business value chain and cement their market share. Several startups have initiated to leverage to disrupt their existing and adjacent industries. The transformative power of AI has been the cornerstone of their exponential growth.
AI continues to be a secret sauce and competitive advantage for startups. Data detonation, lower cost of storage and processing and continuously enriched self-learning machine curated algorithms, AI will continue to be a huge multiplier for startups – by bolstering customer acquisition and retention to improving efficiencies, augmenting the top line and getting embedded across the business value chain of their businesses.
Entrenching Competitive Advantage Through AI
Industry and functional use cases of AI range far and wide. It is imperative that startups first consider their business model to identify the drivers to their business, estimate potential uplift and time-to-value to prioritize the order in which AI use cases are deployed. Here are few areas that can deliver immediate impact.
Understand Your Current Customers
AI can both accelerate the speed and quality with which you understand your current customer base – alongside informing startups of the most opportune ways to serve them. For instance:
- Recommender systems – which are extremely mainstream today. Ecommerce websites are increasingly tapping into the purchase and browsing history of customers, not only to surface their next purchase, but also nudge customers through promotional pricing.
- By using natural language processing (NLP) powered chatbots, startups can very quickly build and scale their customer service function – while ensuring continuous availability at a nominal long-term cost. When combined with sentiment extraction and mining, these ‘intelligent’ agents can pre-process customers’ emotions and provide preferential pricing / promotional offers to customers who have had a negative experience with the startup.
- With AI, startups can capture and re-create customer journey maps – how customers navigate pages, information contained on web-pages and ultimately make the purchase decisions. This can enable startups to build more personalized customer experience on their digital platforms.
Acquire Your Next Customer
In additional to galvanizing CLTV and other retention metrics, AI can also be a crucial part of the customer acquisition process by:
- Improving the accuracy of prospect targeting, by continuously analyzing the drivers of current buyers and mapping them against the cues provided by current prospects – all the while maintaining a lower cost of customer acquisition
- Measuring and benchmarking the success attribution of marketing initiatives and spends – enabling marketing teams to focus their efforts on high-impact marketing activities to continuously drive improved performance.
- In a B2B setting, AI can help judge a browsing prospect’s propensity-to-purchase / act on a call-to-action (based on past users’ actions). This can inform sales teams’ efforts and act as a strong pre-qualification stage in the B2B sales process.
Accelerate Time-to-Market for Products
Beyond commercial functions, AI can also have a transformative impact on the manufacturing and distribution process and help startups realize significant advantages by:
- Pushing closer to 100% on-demand production – through continuous improvement in demand forecasting. This will help create leaner production units, improve predictability in production schedules and reduce wastages due to over-production.
- Using autonomous physical systems for packaging, shipping and warehouse management
- Running smarter and leaner distribution chain – through better demand forecasting at a micro-level, optimizing the size of the delivery vehicle and delivery routes of vehicles (based on inventory shipped) to contain transportation costs.
- Ensuring optimal stock availability at storefront – while balancing wastage due to oversupply and stockouts due to insufficient supply. This would again be incumbent on improving demand forecasting.
Running a tight ship
Finally, given that startups typically operate on very tight budgets and at high speed of execution, AI is a crucial intervention to help them run a tighter ship. While all these tasks are crucial – whether you are a startup or a large enterprise, AI can help achieve outstanding outcomes at a fraction of the cost. This can happen by:
- Speeding up the recruitment process through bots and NLP-powered automated resume scanning. This can reduce the TAT for new hires, by sifting through a large pile of resumes to identify and shortlist the most viable candidates for interview.
- Augmenting the budgeting and financial planning process using AI. Here AI can help going through multiple reports and compiling the findings that eventually inform budgeting decisions
- Automating administrative tasks such as travel planning and front-desk management.
Why AI Is a Game-changer for Startups
Startups cannot afford to ignore the disruptive power that artificial intelligence can bring. AI is particularly suited to be a game-changer for startups because:
- Given the size and scale at which startups operate, it is easier to conceptualize and implement AI-centric solutions – without having the navigate the bureaucracy of multiple stakeholders in the decision process.
- Scalability and continuous improvement are built into the very fabric of AI – investments in AI by startups will see exponential value realization with enriched data sets and refined algorithms.
- The need for speed and cost efficiencies is paramount for startups. For startups to truly disrupt their industry incumbents, speed is of essence. A slow pace of growth usually kills startups before their story even takes wings.
- Having seen examples of corporations who ignored their startup rivals burning their fingers (from Blockbuster and Netflix to Yahoo and PageRank) traditional incumbents are increasingly taking note of technology savvy startups and partnering with them to entrench their market position, through VC’s and startup accelerators. If focusing on channels is crucial to growth in your industry, AI-centric processes will provide a clear competitive differentiation over your rivals.
AI is both a necessity and an important lever for Startups to grow exponentially in their markets. Through AI, Startups will be better positioned to successfully disrupt their incumbents, win market share and customer delight. Startups not actively harnessing the power of AI to achieve speed and manage scale will be doing so at their own peril.
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The brick-and-mortar retail industry is not in a good moment right now. Much of the turmoil in this industry comes from the fact that consumers are seeking a richer and indulging retail experience that reduces friction – much like what they have now become used to as they shop online. Consumers also expect traditional retailers to capture the essence of their individuality – who they are, what they like, and how they prefer to consume information. Retailers need to understand and align themselves with the expectations of the consumers in order to increase profitability and customer loyalty. They need to not only be aware, but also go full throttle for adopting technologies such as AI for influencing and revolutionising customer behaviour.
Retailers need to explore use cases pertaining to exponential technologies to address the disruption that their industry is going through. They need to catch up with how recommendation engines are redefining customer experience, how retail business value chain transformation is shaping up, and how AI can enhance the supply chain aspects of their business. And as I mentioned, awareness is simply not enough – they need to assess and adopt these technologies on a war footing to survive in the world we live in today.
The data-powered disruption of retail
Data in the retail industry is increasing exponentially in terms of volume, variety, velocity – and more importantly – value with every passing year. Smarter retailers are increasingly aware of how every interaction between the business and customers holds the potential to increase customer loyalty and drive additional customer revenue. Retailers that adopt AI today have the potential to raise their operating margins by as much as 60 percent.
But just having the data and building reports that summarise customer behaviour at an aggregate level are not enough. Insights are important, no doubt, but retailers desperately need systems that can make actionable decisions from the data. Insights into average user behaviour is simply too broad. Retailers need to now create a meaningful dialogue with each individual customer that honours their shopper’s preferred level and mode of engagement. This requires more than summarised reports. It requires technologies powered by AI – the ability to minutely understand every customer individually and take actions that each individual customer expects.
We now live in a time where data-driven decisions are extremely pervasive and useful. So much so that the worlds of ecommerce and traditional commerce are seamlessly merging. Every company is now omni-channel. Whether you think of Walmart buying Flipkart to boost their online presence or you take Amazon purchasing Whole Foods to bolster their brick-and-mortar presence. It is not about the web, the app, or the store – it is about having all of those. With the quantum of data available, we’ve seen an extraordinary few years in the retail industry – in the sense that data is actively deconstructing and rebuilding what retail will look like tomorrow. Traditional incumbents need to pay heed to the warning signs signalled by their defunct counterparts and aggressively embrace the data-driven disruption of retail.
AI transforming retail
Predictive analytics has been used in retail for several years now. However, in the last few years – with advances in technology and artificial intelligence – we are seeing the high speed, scale, and value that predictive analytics can deliver. The AI-enabled world of retail helps business transition into a world where consumers are always connected, more mobile, more social, and have choices about where they shop.
Deep learning in commerce
The retail industry is one with a lot of benefit to be gained from deep learning, in part because it’s such a data-rich industry and because there is some momentum around AI gathering already. Further a lot of the AI techniques enjoying success in other applications across industries powered by deep learning are well positioned to make serious impact on retail, streamlining processes, and transforming customer experience into something that largely resembles the experience customers get when they visit online portals.
Deep learning has been the fuel for much of the recent success in applied AI, so it is not surprising that some of the first attempts at augmenting the offline shopping experience have been making use of the power of deep learning in classifying images. If you look at something like Shelf analytics to seek out merchandising effectiveness, you can see the beginnings of how deep learning fits snugly in a retail context.
Now with minimal effort, retailers that can leverage automated AI capabilities will see a strong rise in customer engagement and sales. The best part is – this can be accomplished by data that is already available to them and captured in their enterprise systems. There’s more. The algorithms required for powering these systems, such as collaborative filtering, are relatively simple to deploy and efficient to run.
Intelligent product searches
Another great use case for retailers is leveraging AI for powering intelligent product searches. By using AI, customers can take pictures of things that they see in the real world, or even in an ad, and then locate a retailer who has that item in stock. This can easily serve as the start of a shopping experience. Typically, consumers do often see something that they like, but do not know the name of the item, brand or where they can source it from.
But taking photos is not the only modality for shopping, and there are other areas in the shopping experience where AI can play a part. In online commerce retail, for instance, customers often know what they are looking for but do not know the exact search terms or must scroll through multiple pages of inventory to find it. Deep learning can be of help here as well. Auto-encoding features of images in an inventory based on similarities and differences brings about a rich model of what is available in the inventory, and the model is surprisingly close to how we, as humans, perceive shoppable items. The model alone, of course, is not enough: We need a way to understand a shopper’s preferences as they interact with the inventory.
Speed and communication in real time
Just a few years ago, retailers used to take weeks to analyse customer data and make product offers. Machine learning and AI are changing the game by streaming live data and curating products in real time – based on their understanding of each customer. This significant drop in the amount of time taken between receiving data and powering an intelligent decision is made possible by AI and helps improve the uptake of products from retailers. For instance, by using mobile geo-location capabilities retailers can now offer deals or promotions when customers walk into or near the store (not after they’ve paid at the checkout and departed). Mobile push notifications on company apps allow retailers to track engagement the second it happens.
Given this rapid evolution, retailers have many choices on how to use AI in their marketing and sales strategies. Consumers are seeking a richer retail experience that reduces friction while also capturing the essence of who they are, what they like, and how they prefer to consume information. The sooner a retailer understands this and creates the best consumer experience they can, the sooner they will increase profitability and retention rates. I predict that this retail revolution will continue to unfold and expand over the next several years. But as the industry expands one thing is certain: in retail, personalisation and the customer journey are key, regardless of how you get there.
The ‘segment of one’ approach
A generic, aggregative understanding of customer behaviour is no longer enough. Individual segmentation is the next step for retailers looking to create a super-personalised experience for their users.
The worlds of traditional commerce retail and ecommerce retail are rapidly merging. I think ecommerce retail for many years was an interesting trend, but it was on the side, largely, of what was happening in retail. Today ecommerce retail is less an ancillary part of retail and more about the way business is now done. Online and offline experiences are fast coming together and without an omni-channel experience, it will be extremely difficult for a retailer to survive. That said, I do not doubt there is a future for brick-and-mortar retail, but there will need to be a transformation of retail real estate. Stores are going to become as much distribution and fulfilment centres as they are full-fledged shopping experiences. And they will need to be highly technology enabled.