But what exactly is Unified Commerce, and what does it entail? Unified commerce represents a significant shift in how technology supports the retail industry. When various front-end and back-end technologies seamlessly merge into a single unified platform, technology ceases to be a separate entity. Instead, it seamlessly integrates into the retailer’s business infrastructure, providing an agile system for delivering intelligent and efficient service at every touchpoint in the customer’s journey.

The evolution of shopping: 2013 vs. today

A decade ago, in 2013, my shopping journey involved a visit to the physical mall. Fast forward to today, and I start by launching a video conference with a store concierge, all from the comfort of my home. Thanks to the wealth of data, augmented and virtual reality (AR-VR), and the Internet of Things (IoT) infrastructure, the concierge suggests items by superimposing their images onto my digital avatar. I open another browser tab to research customer reviews and pricing, only to discover better offers at a different store, prompting me to place my order there. This shopping experience continues as I make one online purchase and then digitally visit another store to explore more options and find the perfect gift.

How do you predict an unpredictable customer?

Hearing a customer say, “I was just strolling by and noticed this, and I had to have it,” is music to a retailer’s ears, whether in a mall or a retail store. It’s a testament to the merchandising team’s exceptional job in curating gift options, making the shopping experience delightful. The customer finds a perfect gift basket more quickly than expected, and the checkout process is the final step to complete their visit.

As I reflect, engage in reading, and hold discussions with retail experts like  Kushal Kumar – Vice President of Strategic Delivery Organization at Indium Software – on how retailers aspire to craft extraordinary and personalized “unified” customer experiences, I believe that creating serendipitous moments like the one described above plays a crucial role.

As retailers navigate the opportunities and challenges presented by the COVID-19 pandemic, the seamless integration of digital and non-digital strategies becomes instrumental in gaining a competitive edge and future-proofing their businesses. Here, we explore three trends shaping retailers’ adoption of a unified commerce strategy.

#1 An Irresistible Opportunity – The Emergence of New Fulfillment Options

Prior to the COVID-19 epidemic, the proliferation of Buy Online, Pick Up In-Store (BOPIS) and curbside pickup was already on the increase. Nonetheless, the crisis has spurred consumer acceptance of these services. Online holiday sales in the United States climbed from $257 billion in 2021 to $270 billion in 2022, but worldwide holiday sales stayed flat at $1.14 trillion year on year. According to Salesforce, the development of the “buy online, pick up in-store” (BOPIS) option was a big cause behind this rise, with roughly one in every five worldwide online holiday orders.

An efficient unified commerce framework handles the inherent difficulties of operational management, from storefronts to the back office. Customers, for example, may always get real-time price and product availability. Integration with order and financial data provides clients with a complete record of all their cross-channel purchasing actions, including purchases, refunds, and exchanges made in physical stores and online.

Retailers are better positioned to improve the consumer experience by collecting data across the shopping journey by meeting customers where, when, and how they choose to make purchases. Furthermore, it gives useful insights that allow for the execution of a unified and seamless consumer and brand experience, both online and in-store.

In the words of a customer: “The app indicated that there were only two remaining – and there were two on the shelf.” Customers in the alternative situation just hope that the system’s accuracy matches the in-store reality when they arrive to make a purchase.

#2 Meeting the Demand for Personalized Experiences – Preparing for Tomorrow

Brick-and-mortar stores need a technological architecture that delivers insights akin to online experiences, extending beyond inventory display. Providing such information is paramount in today’s consumer landscape, where purchases are driven by lifestyle, value, and individual preferences. Customers now expect immersive shopping experiences enriched by virtual and augmented reality.

Retailers that tailor their services to individual customer preferences can offer personalized recommendations at every stage of the buying journey, guiding customers toward a successful checkout. To ensure a seamless experience, retailers must ensure the most relevant products are readily accessible, from fitting rooms to endcaps to BOPIS pickup points. Real-time sensor data, machine learning (ML), and artificial intelligence (AI) can revolutionize these processes, providing live inventory information, SKU-specific stock levels, and insights into consumer interactions with products.

Unified commerce amplifies the in-store shopping experience by consolidating consumer data from various channels into a single view, accessible to store executives through mobile POS devices. Yet, true success relies on the ability to collect and translate data into meaningful insights. Integrating intelligence at every stage of the commerce journey is vital for delivering unique and context-aware shopping experiences.

#3 The Growing Tech-Savvy Millennial and Gen Z Customer Base

As consumers entrust more of their personal information to their smartphones, digital payments are swiftly becoming the preferred payment method. Mobile payments offer immediate checkout, as a mere touch of the payment app icon triggers the transaction. In contrast, traditional POS systems take several seconds to approve and process chip credit card payments. Though seemingly minor, these seconds hold significant value for the modern omnichannel customer.

Beyond saving time, mobile payments profoundly influence a retailer’s brand. Retailers that are slow to adopt cutting-edge technology risk being perceived as outdated and out of touch. This is a critical concern, especially considering the preferences of a new, younger generation of customers who favor contemporary technology and expect businesses to keep pace.

A unified commerce framework’s adaptable design forms a robust foundation for engaging with state-of-the-art mobile technologies. By extending digital wallet support to various payment providers, unified commerce ensures the fulfillment of essential payment needs and provides comprehensive support for seamless purchasing experiences. Consolidating data and processes affords retailers the visibility needed to gain deeper insights into customer-facing aspects of their business, enabling continuous improvement and effortless integration with modern mobile technologies.

Concluding Thoughts

“I misplaced the receipt but can quickly find it on my app.” This level of convenience is exemplified by industry leaders like Amazon, Alibaba, Starbucks, Macy’s, and Lowe’s. Retailers must shift their focus from just the product to the individual customer, continuously monitoring customer share-of-wallet and lifetime value, transcending omnichannel strategies to excel in the intense competition.

To achieve this, retailers should adopt an open, secure, and agile platform that amalgamates retailer and third-party data sources and deploys advanced technologies such as AI and ML. This approach offers unparalleled visibility into operations and invaluable shopper insights. Regardless of their purchasing intent, whether it’s finding the perfect gift, selecting the ideal outfit, shopping for groceries, or decorating their homes, consumers crave surprise and delight. They desire hyper-personalized experiences.

Notes

Global Online Sales Top $1.14T During 2022 Holiday Season, Salesforce Data Reveals

The post 3 Serendipitous Ways Unified Commerce Wows Shoppers with Hyper-Personalized Experiences – Retailers Have a Catch! appeared first on Indium.

Some of the key drivers will be the increasing disposable income that has led to an improving purchasing power of people. The convenience of ordering products online on e-commerce platforms and retail stores will further stimulate market growth.

To meet this growing demand and understand its customers better, e-commerce businesses are increasingly investing in advanced business intelligence and analysis tools. This can provide insights into which products are moving fast, in which markets and how to improve their operations to service the customers better, maximize profits and gain a competitive edge.

3 Focus Areas

E-commerce analytics falls into three main areas:

• Data Visualization and Descriptive Analytics: Dashboards created using historical data of customer behaviour and sales records provide snapshots of all key metrics for improved decision making
• Predictive Analytics: Using churn prediction, market-basket analysis and the like, e-commerce marketplaces can predict the demand for products and design promotions to cross-sell and upsell for improving sales and customer engagement
• Cognitive Analytics: Video and images are analysed for product classification based on predefined parameters to quickly upload new products and avoid errors and time delays associated with manual intervention

Challenges and Benefits

For e-commerce platforms and online stores of retail outlets, an understanding of which products are moving, where their customers are coming from and what their customers are saying are very important.

When a product is performing well, they can boost it further by creating suitable marketing collaterals and also pair it with likely related products to increase the overall sales and growth.

A product which is not performing well will need equal efforts to promote and special offers and discounts to increase its visibility can be designed to improve its sales.

Based on geographies, retail businesses can also plan their campaigns for their stores in those locations and step up promotions for those geographies where they have a presence but not as many footfalls.

Machine learning and artificial intelligence can be used for cross-selling and upselling of related products. For example, when someone is purchasing a mobile, relevant accessories can be displayed to encourage customers to purchase a mobile case or headphones, and so on. When a customer purchases a particular model, they can be tempted with a higher model with better and more features.

Analytics can also be used to understand conversion rates from footfall to sales and the insights used to improve the conversions. Reviews, both positive and negative, are a storehouse of information on what works and what doesn’t.

Negative Review Analytics helps to build the product line with quality to meet customer expectations. Sentiment analysis allows the e-commerce players to build on their strengths, rectify their weaknesses and retain the unsatisfied customer.

For instance, in an e-commerce site, a particular bag was very popular but soon, negative feedback started pouring in. On analysis, it was discovered that the bag was still good but a flap that was added as a design element was made of a different material that did not last long as expected. This is valuable input for the e-commerce marketplace as well as the manufacturers to improve.

Competitor analysis can also be used to devise marketing and, more importantly, pricing strategies to improve the edge over business rivals. Marketplaces and FMCG can especially benefit from this.

Use Cases

Indium used sentiment analysis for a sports retailer where the reviews were analysed to understand customer perception and feedback of the products. Indium’s proprietary data extraction tool, Tex.Ai enabled extracting key phrases to gain insights on customer views. This helped the sports retailer improve on its design and customer service.

For an e-commerce aggregator, Indium used teX.Ai to automate product classification.

Chats with customers, either on chatbot or by a customer executive over the phone can be another rich source of insight into customer satisfaction levels. Using data extraction, the discussion can be analysed for what the customer needs, how it was responded to and if it had been concluded to satisfactorily. This is crucial in building customer loyalty and training the executives and the chatbots to ensure there is a closure.

Analytics can also be used for resource optimisation to reduce the waiting time of customers trying to reach a representative.

Indium Advantage

Indium Software, in its more than two decades of existence, has been providing holistic solutions on cutting edge technologies. It has carefully built a team that is a judicious mix of domain and technology experts.

Our e-commerce team can set up and run a marketplace from the ground up using the latest technologies including in-build analytics. It can also build solutions for analytics on existing platforms using machine learning and artificial intelligence. Strong solution architects, subject matter experts and expertise in analytics make Indium an ideal partner for e-commerce platforms and retail brands seeking to leverage the World Wide Web.

The post Analytics in E-commerce and Indium’s Expertise appeared first on Indium.

Being a marketer, one would recognize the immense power of data. Never before have we had access to data like we do today.

For many organizations difficulties arise in collecting, integrating and storing the data. However, making use of this data to drive better business decisions gives organizations a competitive advantage.

And I sure am not talking about reporting here.

Of course it’s intriguing to know what happened in the past and those monthly excel sheets might even get read once, but the organizations that use this historical data to focus on the future and predict future outcomes are the organizations that are surging ahead by leaps and bounds and are discovering enormous value.

When you look at the world of data science today, there is a lot of sophisticated work happening in the field that may be beyond your scope of understanding.

But, Predictive Analytics is something that is within reach for just about anyone and is waiting for it’s advantages to be exploited.

To put it simply, predictive analytics is making use of historical data to predict the likelihood of future outcomes.

The major case in point is increasing your measure of success because you can optimize anything that can be measured or defined.

Predictive models are very different from descriptive models – which can tell you what happened in the past, and diagnostic models – models that can explain or provide rationale as to why something happened.

Now that you know what Predictive Analytics is about, you should be intrigued about it’s applications.

We’re going to see 5 applications that will get you thinking about how you are going to make use of data to boost performance across various verticals in your organization.

Conversions – Yes, we’re all chasing conversions. At the same time it is critical to know who is converting and this is exactly where understanding and targeting the right prospects comes in to play. With the wealth of customer data already in your possession, predictive analytics can help you with quite a few things.

• Customer Loyalty : Predictive models will help you understand what segments and behaviors point towards the tendency to keep on consuming your products and services. Predictive models also help you understand the behaviors and attributes that are likely to cause a switch to another brand.
• Lifetime Value : As you’re scouting for new prospects and evaluating the existing customer base, you can make use of your data to forecast the net profit that will be accredited to the entire future relationship. How this helps is you can target your outreach, marketing campaigns, bonus/loyalty programs etc. more accordingly.
• Churn : Losing customers is never good for business. However, predicting the risk of a customer abandoning your brand can help you drive more targeted and personalized retention programs.
• Market Basket : The checkout basket can be turned into an advantage with the use of predictive analytics. You can understand which products are purchased together and which are likely to be purchased one after the other. This helps you identify your buyer’s purchasing behaviors.

Marketing budgets are better allocated when predictive analytics is used. The newest tools in the market, the best techniques when combined with the bundle of data being generated via every click and impression is a huge opportunity to make sure every marketing dollar is well spent.

• Marketing/Media Mix : There are lots of channels, up and down the funnel where you are likely to spend money. Being able to credit each touchpoint with value in the purchase path and predicting the budget allocation can help you attain more performance out of less spend.
• Audience Targeting : The “spray and pray” targeting tactic has become old school as today, we are gaining more and more data about who may become a customer and where we can find them. Predicting the probability of someone in the audience converting to a customer and the value that they bring can help the targeting become more precise and lessen the marketing dollars being spent.
• Purchase Intent : Usage of customer data/behavioral data to predict the intent of purchase for any lead/prospect can be immensely valuable to an organization. This can also be modeled to predict digital’s role in driving offline sales.

If you are investing in digital assets like websites and mobile apps, it only make sense that you’ll want to make sure that you’re getting the most from them.

Predictive Analytics can help you understand what factors will result in the best content, what areas can be customized to particular users and which areas of the digital experience are ideal for optimization.

• Content optimization : Time and resources are spent on creation, development and maintenance of content and it we have a lot of data about how the content is performing. From this data, pulling out factors that have been successful will help guide your content strategy in a way where you will produce pages and experiences with a high likelihood of achieving the set goals.
• Personalization : The combination of digital experiences and customer data results in you starting to segment and predict which group of users is likely or not likely to respond to your messages, offers etc. Today, the personalization tools give you the power to achieve user level customizations to give people what you know they are likely to want.
• Testing Strategy : A/B and multivariate testing is not a new phenomenon but the difficult part of testing is figuring out what to test. Predictive analytics can help you understand which grey areas of the experience need maximum improvement and it also helps define a hypothesis. Apart from providing a better experience for the users, the results can also feed the model for improved accuracy.

Risk is a very broad category. In reality though, all organizations try to mitigate risk with every action of theirs.

Data is used to pin point the factors that tend to create risk and then predict unwanted scenarios that are likely to occur in order for you to come to terms with the unknown and mitigate consequences.

• Fraud : This one is for the e commerce space where a lot of work has gone in. Organizations can use their own data in order to evaluate factors that are likely to be associated with fraudulent activities and in addition they can address these issues by improving security by adding more steps for checkout, selective payment options etc.
• Collection & Recovery : The accounts receivable has a direct impact on your cash flows and making sure you have a handle on accounts receivable is imperative as it also affects the organization’s ability to operate. Predictive analytics can help identify at risk accounts and will help formulate strategies that mitigate collections risk and have high success rates.
• Pricing : Pushing a product out in the market is influenced by price. With a price too high, there is the risk of acceptance and volumes ; with the price too low, profitability becomes an issue. Prediction of price elasticity, pricing gaps, thresholds and profitability targets can be done with the help of existing products and competitive data. This will help you arrive at an optimal price point.

Marketing and customers are extremely important, yes. However, at the end of the day the products and services have to be delivered with maximum operational efficiency.

Demand prediction to Supply chain management – Predictive analytics can prove to be an integral part of the planning and execution stages of operations.

• Forecasting : Be it planning of production cycles, demand predicition for new products and services or estimating financial performance, historical data can be used to model plausible scenarios or outcomes. Those models can be manipulated to understand what should be done now to impact the results you are most likely to see in the future.
• Network Optimization : Networks can mean many things, this may include supply chains, processes and just about anything that has inputs, outputs and dependencies. Using the data to work around the factors that influence the efficiency of each node within the process will help find the optimal paths through them.

These are just a few areas in which organizations can leverage the power of predictive analytics to make informed decisions about future states.

The tools and technology available today make these analyses accessible to almost every organization.

What’s left to do? Identify a business challenge, evaluate the data you have to work with and finally come up with a modeling solution that will help you see the future and make decisions driven by insight.

The post 5 Ways how Predictive Analytics can help you appeared first on Indium.