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Grocery Replenishment Has Evolved: 9 Things Every Retailer Must Know

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Date: 23 Jun 2022
Merchandising and Supply Chain
Blogs

Grocery Replenishment Has Evolved: 9 Things Every Retailer Must Know

Grocery retail has irrevocably changed. The last two years in particular have exposed many gaps in business. Predictive replenishment emerged as one of the major fault lines in the changed grocery retail environment with many retailers unprepared to address challenges such as frequent out-of-stocks, increasing inventory costs, wastage that comes with fresher and newer products, omnichannel nature of business, and shift in customer behavior.

Here are nine best practices for grocery replenishment that are fast catching on and can help you build a robust replenishment framework:

  1. A Good Demand Forecast Is Essential, However It Is Not the Only Critical Input
    A good replenishment planning solutions system must also factor in existing inventory balance, expiration dates, open orders, average lead time, minimum order quantity, standard ordering frequency, and other data points that are key to effective planning.
  2. Simulations Will Help to Hone Your Replenishment Strategy
    Both demand and supply-side fluctuations characterize your supply chain. Monte Carlo simulations can create thousands of different ‘what-if’ decisions to develop supply chain scenarios. These scenarios can help you optimize your resources (cost), improve customer service, and strengthen your competitive approach with a robust replenishment strategy.
  3. Leverage Optimization Algorithms to Get the Right Outcome Every Time
    Artificial Intelligence in ai replenishment planning has proven effective in dealing with large-scale multifactorial optimization. AI can do the heavy lifting of identifying predictors and best-fit models for demand forecasts, and optimizing order plans for supply chain factors.
  4. Factor in Supply-side Variations
    In the past few years, grocers have realized that disruption in supply is an emerging threat and needs a mitigation strategy. Providing for probabilistic treatment of supply-side variations in your replenishment framework can go a long way in achieving that.
  5. Collaborate With Your Supplier Base
    Your replenishment framework is as strong as the weakest link of your supply chain. Even the best replenishment planning framework will fail if the supplier collaboration is poor and cumbersome. Think of supplier integration as part of replenishment planning.
  6. Safeguard Against Risks With More Effective Guardrails
    While accounting for expected risks such as delays in lead time during holidays, you must also be prepared for unexpected events such as inclement weather, war, and epidemic/pandemic. Setting up dynamic guard rails such as minimum inventory optimization turnover period as opposed to static ones like minimum safety stock levels can safeguard your operations.
  7. Your Shelves Might Not Be Functioning The Way You Assume
    First In, First Out is a fair assumption for ambient products. However, for fresh food categories, it is probably the opposite. Being astute, buyers select produce, meat, fish, poultry, and dairy that show no signs of spoil. They might anticipate that the grocer has placed the oldest inventory at the front and reach further back of the display.When this behavior becomes a common practice, only dead stock remains on the grocer’s shelves. Hence, incorporating batch-wise stock balances and expiration dates in your retail replenishment software will avoid wastage and keep your shelf looking fresh.
  8. Set Proactive Alerts To Act Preemptively
    To ensure you order the right quantities at the right time, simplistic alerts such as the ones based on fixed pre-expiration timeline might not be good enough to react to grocery scenarios. Instead, a proactive notification based on a combination of factors—such as inventory demand prediction, stock balance, and product expiration— will help you course-correct in a timely fashion.
  9. Let People and Technology Augment Each Other
    While advanced technologies, such as AI and ML, augment the replenishment planning processes, there are certain limitations that are addressable only by human intervention. Make sure there are humans in the loop who can intervene as needed. People and technology augment each other well!

AI-powered Replenishment Planning Solution Tells You What, When & How Much to Order

Advanced technologies, such as AI and ML, bring revolutionary capabilities to the table across demand forecasting and automatic store replenishment – helping planners make snap yet faultless decisions every time.

One such solution is ADA Global’s Order Right, which helps planners shift their focus from tedious, manual number-crunching to 1-click intelligent order planning.

Order Right generates accurate SKU level order plans with its proprietary optimization algorithms that account for key supply chain and category factors such as shelf-life, lead-time, MOQ, etc. while constantly monitoring stock balance, sales, and demand predictions

Table Of Contents

Unpacking the First Real-time Customer Data Platform (CDP) Made for Retail

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Date: 21 Jun 2022
Omnichannel Marketing
Blogs

Unpacking the First Real-time Customer Data Platform (CDP) Made for Retail

Data is an immutable asset. One that has surpassed oil to become the most valuable resource in the world. Yet, in the age of analytics, it is just that – fuel. Much like fuel, it must be transformed to extract genuine value.

As Clive Humbly put it, “Data is the new oil. It’s valuable, but if unrefined, it cannot really be used. It has to be changed into gas, plastic, chemicals, etc., to create a valuable entity that drives profitable activity; so must data be broken down and analyzed for it to have value.”

Though infinitely available (unlike oil), the challenge most marketers face is the inability to effectively unify, analyze, and leverage retail customer analytics to drive decisioning and personalized customer engagement.

Customers are loyal to brands that acknowledge them as individuals. This makes the ability to hyper-personalize every customer engagement paramount for retail marketers.

To help marketers activate their audience in real-time with custom offers and cross-sell products along the customer journey, we are introducing our Real-time Customer Data Platform (CDP).

A hurdle most marketers encounter in their efforts to deliver personalized experiences is unstructured data silos. These inconsistencies in data become barriers to accessing a comprehensive view of the audience. Consequently, stakeholders are unable to identify, segment, or even market to the audience along their journey. This leads to bigger problems such as the inability to quantify performance and contain churn.

ADA Global’s retail customer data platform empowers marketers to unlock the full value that can be derived from data and orchestrate high-quality journey-based campaigns. Our CDP paves the way for data unification and intelligent decisioning through real-time audiences and delivery of one-to-one personalization engagement across online and offline channels. In addition, native integration with ADA Global’s Campaign tool enables marketers to deliver tailored omnichannel marketing campaigns across the customer journey.

How Real-time Customer Data Platform Works

Our API-based integration facilitates frictionless consolidation of the tool to existing systems at scale.

CDP’s array of connectors eliminates data silos with out-of-box batch and real-time integration that seamlessly centralizes data from any app or offline data store.

Anonymized customer profile data (including demographic, behavioral, and transactional) is captured and ingested in real-time from online, offline, first party, second party, and third party systems. This is then stitched together to create a unified customer profile that is updated with each customer interaction.

Profiles are dynamically real time customer segmentation using out-of-the-box models for churn, recency frequency, order value, engagement, etc. and the audience thus derived can be activated at scale.

Audience Discovery helps marketers filter these segment lists using attributes or browsing information. Events can be added to further refine the audience to create granular segments for targeted engagement.

CDP’s Audience Manager creates, manages, and leverages the audience segment list to drive contextually relevant journey-based real time customer engagement in real-time across touchpoints.

Our diverse mix of connectors for outbound engagement empowers marketers and retailers to reach their audience across channels. This includes Email, SMS, Messengers, and Social.

Here are a few ways how marketers can leverage ADA Global’s CDP:

1 Real-time cross-channel profile updates

The real-time customer data platform enables dynamic updates to the real time customer profile with every interaction, both online and offline. Actionable algorithms enable real-time audience activation so marketers can deliver journey-based hyper-personalization in the moment.

2 Real-time transaction communication on Whatsapp

Leveraging ADA Global’s Campaign tool, marketers can enable triggered email marketing at various event nodules. Marketers can determine triggers, and delays (days or minutes post the event), as well as choose from templates how they would like to communicate with their audience.

3 Proactive action to arrest churn

Churn modeling in-built into our CDP enables marketers to predict the probability of churn and identifies drivers which then can be leveraged to take corrective action. This audience list is then pushed to Campaign to initiate dynamic content personalization retention campaigns to reverse churn behavior.

There are a lot more use cases you can explore. Review the full list of power-packed features and learn how we can help you improve customer engagement, loyalty, and lifetime value. Or request a demo here.

Table Of Contents
How Real-time Customer Data Platform Works
Here are a few ways how marketers can leverage ADA Global’s CDP:

Best Practices to Boost Your Email Marketing Campaigns

Posted on by
Date: 31 May 2022
Omnichannel Marketing
Blogs

Best Practices to Boost Your Email Marketing Campaigns

So, you put in the work, try to optimize where you see opportunity, and leverage new techniques. You’re excited about this email. Yet, you don’t see the results you were expecting.

It’s frustrating.

In today’s social-media driven world where everything is quick and easy, it is forgivable to assume that email marketing is past its glory days. Yet, this couldn’t be further from the truth.

Marketers still vouch for email to deliver results and here’s why:

 Source: campaignmonitor.com

But the question remains – how do you improve your email marketing campaigns? Where do you start and how do you go about it?

In this blog, we will cover how you can make the most of your email channel and the best practices in deliverability, personalization, and dynamic content personalization.

Let’s dive right in.

When is the last time you saw an email that resonated with you? One that had you hooked right from the get-go. One that covertly but seamlessly navigated you to click on the CTA button. The brilliance of a well-crafted email that leads to conversions is its simplicity in encouraging action.

While there is no definitive formula to craft the perfect email, here are a couple of pointers to help get you started:

  • Focus on Your Key Objective For The Email: While it’s tempting to cram in as many ideas and offers in one email, it is important to hone in on one objective. This is the theme of your email around which everything else falls in place. Imagine crafting an email centered on trying a new holiday theme recipe. However, the email also includes content about passes to a concert. Confusing, right? Always begin with a clear objective. Your content and design must speak to this objective.
  • Maintain a 60% Text to 40% Image Ratio: It’s important to structure your email in a way that appeals to the reader. No one has the time to read chunks of text in an email. It is off-putting and can result in a dip in your KPIs. Conversely, an email laden with images can come across as too promotional. Maintain a healthy balance of text and image that conveys a message than a sell.
  • Keep Your Subject Line Short And to The Point: Your subject line should not exceed 41 characters or 8 words. This is the first interaction your audience will have with your email – if it doesn’t convey the message on the first read, the reader will most likely ignore the email.
  • Your Subject line, Content, And Visuals Need to be Aligned: Imagine a subject line that speaks of an exclusive summer offer, but the banner contains winter cues, while the main content holds information about an upcoming launch. It is haphazard. It will not translate. Nor will it meet its objective for the campaign. The messaging right from the subject line to the CTA must all be aligned.
  • Include a CTA in Every 3rd Fold of The Email: This draws attention back to your objective and keeps the reader engaged. This also aids in improving the conversion rate of the email.
  • Ensure Your Email is Responsive And Within the Recommended File Size: Bear in mind that your audience accesses and views your email on different devices. With over 60% of emails being opened on mobile devices, it is important to ensure that they are responsive and load with ease. The recommended file size is 80-100 kb.
  • Use Valid Links With UTM Tags: Ensure that the links used in your emails contain no typos or errors. It is especially important when your CTA links to a different landing page or sign-up sheet. An error in your link may cost you conversions. Remember to add UTM tags in your emails to track its progress across online platforms. Data is key – gather, analyze, and optimize to make the most of your email channel.

While the guidelines above will help you speak your message to your audience, it is also important to speak to your audience.

Here are three key factors to help you clinch gold in your email marketing efforts:

  1. Experiment With ai email personalization: Gone are the days when a cookie-cutter mass mailer did the trick. Your audience wants you to speak to them on a personal level. They want to form a bond with your brand. It is this connection that will drive engagement. Here is how you can experiment with personalization:
    • Lifecycle Email Campaigns – Based on the different life stages in your audience’s journey, create and share emails relevant to each segment. For example, share a welcome email as soon as someone subscribes to your newsletter. Or share an email reminding them about their cart. It is important to cultivate your relationship with your audience at each stage. Sharing emails at each nodule engages them and helps to motivate them along their lifecycle.
    • Always-on Campaigns – Share emails to your subscriber list based on their individual loyalty points accrued, offers based on past purchases, as well as festive offers to help keep them engaged with your brand.
  2. Drive engagement With dynamic content platform: The more relevant an email is to your audience, the more value you add to your campaign. Dynamic content empowers you to offer your audience a personalized experience at scale. The dynamic content you can leverage to increase the effectiveness of your campaign are::
    • Text – It is the most common and effective type of dynamic content facilitating personalization. You can personalize a line, a sentence, or even a paragraph to different subscribers. For instance, if you are a clothing brand, based on past purchases, you can create a dynamic paragraph on clothing and footwear that can be personalized for each audience segment.
    • Visual – You can change the image, GIFs, and videos based on the segments you target. You can also experiment with various visual elements based on your segment.
    • CTA – You can customize the size, shape, color, and text within your call to action to drive your click-through rate and result in conversions.
    • Offer – Include customer offers in your hyper-personalized email campaigns based on seasonality, gender, interests, available inventory optimization, timing, and other personalization variables. For instance, let’s assume you run an email campaign with an offer for shoes at a discounted price. Say this email resonated with the people from Japan (in the JST time zone) and they made use of the discounted price. Consequently this resulted in you running low on stock. The people in the Pacific Time zone have yet to view this email. This gives you a leeway to switch the offer with a discount on another product. So, when a person from the PST time zone views the email, they will see the new offer.
  3. Analyze and Enhance Deliverability: With elements of the email now in place, it’s time to dot your ‘i’s and cross your ‘t’s. Deliverability does have an impact on the performance of your triggered email marketing campaign. Here’s how you can avoid dips in your performance owing to low deliverability:
    • Authenticate – Ensure that your SPF, DKIM, and DMARC records are in place. This will help internet service providers verify that your mail server is authorized to share mass emails.
    • Monitor – Routinely check if your brand or sender ID figures on any blacklists. Review your brand’s score in senderscore (you want to fall in the 90-100 percentile), and errors in postmasters, to ensure that you are clear of any shortcomings. This will help ensure that your emails go out without a hitch.
    • Act – It is important to monitor and make changes as your campaigns progress. When you notice a spike in the number of unsubscribes, it is wise to reduce the frequency of emails being sent. This helps to sustain your sender reputation until you can adopt a different strategy. Routinely purge the ID with bounces and non-openers that show up after 180 days. This will help you maintain a healthy list and in turn healthy email campaign performance.

In summary, email continues to remain a robust and effective digital marketing tool. When executed the right way, a successful email marketing campaign can lead to a great customer experience and higher sales. The vast opportunities available with email give you the ability to experiment and improve as you go. Don’t be afraid to get creative with your emails (emojis add flair too!). In closing, always remember to adapt, test, and optimize!

 

Is Your Commerce Search Traditional and Not Personalized?

Posted on by
Date: 11 May 2022
Digital Experience Personalization
Blogs

Is Your Commerce Search Traditional and Not Personalized?

Use this checklist to find out.

Do you understand various search behaviors?

Like most retailers, you are seeing a greater influx of visitors – both new and old. Over the past 24 to 30 months, there has been an appreciable shift in shopper expectations, needs, and behaviors. So, is your commerce search keeping up?

To analyze this better, let’s take a step back to understand visitor behavior. Even as your visitors traverse through various stages of their customer journey, they exhibit different behaviors in how they use commerce search. For example, visitors who know what they want demonstrate a very different pattern of searching compared to those that are not sure what they are looking for and need assistance or even those that are looking for a solution to address a challenge but don’t know how.

  1. Exact Search Terms – e.g., Configuration and model number of a gadget or a search such as ‘Sneakers made of recyclable material’ of a certain size and color from a particular brand.
  2. Search with abbreviations – e.g., 15’ or 15 ft or 3 Ounces or oz
  3. Misspelled words – Broccolli or Vaccum

  4. Alternative words – Bed sheet or bed linen

  5. Plurals – Sunglasses or Shades

  6. Search terms based on a problem – ‘Dry skin’ instead of ‘body lotion’

    Are you able to home in on visitor intent?

  7. Shopper searches can reveal high intent signals. If you can pick them, you can dramatically improve the relevancy of your search results for the shopper and prevent ‘no results’ situations.

    Some terms are more important, e.g., 12″ sky blue sleeve (where 12″ is as important as the color of the product), ‘Organic’, ‘Gluten free’, etc.

  8. Your shoppers’ collective searches are revealing as well. They can help you determine associations between categories, products, and search terms. The best part is that algorithms that can self-learn can make these associations without you needing to write rules manually or change product data.

  9. A shopper’s affinity for custom attributes like style, pattern, size, color, or preference around brands and products can be understood not just from their current session but their history as well. Personalizing search based on this information can drive higher conversions.

    What are you doing to aid visitors to explore the most relevant products in your catalog?

  10. Sometimes, visitors are unsure as to what they want to purchase. This is your opportunity to guide, inspire, and even surprise them with meaningful yet interesting search results. This is what separates plain vanilla searches from personalized commerce searches.

    As shoppers begin typing into the search box, you could predict what they may be looking for and provide some inspiration.

    Increasing search result relevancy with each keystroke makes for better experiences.

  11. When products need multiple dimensions for selection, contextual filters can help quickly narrow down results.

  12. With 70% of traffic coming from mobiles and the limited real estate that these devices afford, you need to be able to personalize commerce searches. You want users to find what they are looking for faster. And that means your search needs to be contextual and display the most relevant items at the top of the results. We use a metric called Findability to measure if shoppers are finding what they need.

In other words, commerce search must be simple, intuitive yet sophisticated. It must reduce the need for users to scroll or use filters, and thereby help them complete their journey quicker.

The commerce search box is the perfect ally for marketers and merchandisers in helping deliver the best experience and boost conversions without relinquishing control over aspects like stock counts, trade promos, and private label growth metrics.

Commerce search can be really potent for retailers if it’s personalized. So, how would you rate your commerce search based on these 12 points?

Learn how you can move from legacy, one-size-fits-all search to self-learning personalized search.

Experience Browser – Adding Intelligence to Web Personalization

Posted on by
Date: 5 May 2022
Digital Experience Personalization
Blogs

Experience Browser – Adding Intelligence to Web Personalization

It is not just enough to know if personalization is working on your commerce site. Apart from displaying the right content or products to shoppers, you should also know why and how they are being shown. But do you?

As marketers aim to drive higher visitor engagement, delivering personalized customer experiences has always been a top priority. The more individualized the experience, the higher the engagement and better the conversions. By investing in artificial intelligence and machine learning for e-merchandising teams, companies want every website visitor to have their own unique experience, rather than defining a generic, one-size-fits-all experience.

AI solutions these days, rather most of them, are made for automation. They are not designed to build on existing capabilities and the accumulated intelligence internal to the business. There is also a definite lack of visibility into how they arrived at an outcome. Such ‘black box’ solutions are quite simple, and thus do not provide business controls, visibility, and extensibility, thereby limiting the companies from uncovering the best outcomes using AI.

Xen AI, through the Experience Browser, opens this “black box” and gives you visibility into how the system works, giving you unprecedented control over areas to improve and explore, test new possibilities, and deliver the best outcomes.

The Experience Browser: AI Transparency the Way You Always Wanted

Understanding why and how an optimal experience was selected for an individual is key to aid decision-making for business users. That is exactly what XB, or Experience Browser provides – a keen insight into how Xen AI is driving performance.

The XB UI sits on top of the customers’ websites and gives you actionable insights into how dynamic personalization experiences are being created. Traditional tools do not even come close to having such functionality.

An intuititve visual overlay enables business users to edit and audit AI decisions using a single click on aspects like unified customer profile, summary of recommendations, dynamic segments, rules involved, and strategy evaluation.

Picking Individuals From the Unified Customer Profile

All the interactions that any specific individual has had with your brand can be viewed through the XB. Since all the data does not include personally identifiable information, whatever analysis you conduct and insights you derive don’t step upon the privacy of your customers.

All searches, the different dynamic segments they belong to, declared and derived preferences and affinities, clicks, prior purchases, and standard geo-location data if shared are available at a single click.

For companies that run omnichannel businesses, matched real-time customer profile with offline and store purchases can be depicted by the XB.

Understand the Strategies Used to Drive Decisions on Every Placement

Multiple areas in any website can be personalized. These areas include, but not limited to, product placements, content personalization platform, search overlays, and promotional offers. Through the XB, one can easily understand why and how the decisions were taken by the Experience Optimizer, the heart of the Xen AI engine.

In short, multiple strategies are measured against one another in a competitive match by the Experience Optimizer and the best one is chosen as per the context in real-time. After applying the merchandising rules, the one-to-one personalization experience is determined for every customer.

Along with the strategies picked by the Experience Optimizer and the rule selection, the Experience Browser also shows the reason behind that particular selection and a progressively filtered result set.

Real-time Trends on the Website

At any given point in time, you can use the Experience Browser to view the product trends as they happen, in real-time, including views, clicks, and purchases on the website. Holiday or seasonal sales preparedness can benefit greatly from this.

In terms of products viewed but not purchased, the insight gained can help merchandising teams determine boost versus bury rules.

Deep Links to Experience Insights

The presentation of real time customer data profiles reporting and multi-dimensional analytics provided by the XB nicely complements the ADA Global dashboard. Easily accessible reports for sales attribution, slice-and-dice by segments, cohorts and more across key metrics – attributable sales, revenue per visitor, session, etc. are just a sample of the insights that you can derive from the XB.

Content Performance Rankings And More

Visualizing dynamic content personalization performance at a glance is really handy. This is also made possible by XB. Drilling down to details like which pieces are being used where and how much is the performance affected by any kind of placement change. In a few clicks, you can determine engagement as well as conversion through the XB. Understanding how much revenue flowed in due to a creative asset is a unique capability offered by the XB.

Experience Browser Is Imperative for Your Holiday Readiness Plan

Having visibility across your strategies and their efficiency in driving personalized product recommendations is key to generating more conversions. That is exactly what the XB helps you achieve. Club that with your holiday readiness initiatives and you have a recipe for success.

More Reading:

Check out our Guide to Omnichannel Personalization to learn how to integrate your online and offline channels and deliver a unified experience to consumers.

9 Best Practices in Demand Forecasting for Grocery Retailers

Posted on by
Date: 27 Apr 2022
Merchandising and Supply Chain
Blogs

9 Best Practices in Demand Forecasting for Grocery Retailers

Download this article as a PDF

Grocery retail supply chains are getting more complex and unmanageable with traditional forecasting models. Matching supply with demand for a broad inventory that includes fresh and short shelf-life products on one end and ambient products on the other is not easy. Add to that the complexity that arises due to changing consumer behavior, who have started to incline towards convenience and price over brand loyalty. Yet traditional demand forecasting is still heavily reliant on constant monitoring and intervention from a supply chain expert.

Accurate and agile demand forecasting lies at the center of grocery retail’s customer-centric yet lean approach. Doing forecasting right has far-reaching benefits:

  • You reduce your wastage by better inventory demand prediction planning
  • Your displays look attractive and dynamic
  • Customers get fresher goods
  • You sell more by placing your product at the right place at the right time across channels

So, let’s look at nine secrets to improve your demand forecasting and take it to the next level.

Account for Dynamic Demand Forces With Multivariate Forecasting

With price sensitivity and convenience changing the way consumers shop, the demand for products has become much more volatile and difficult to predict with simple models. It has therefore become imperative for grocers to enrich data and not simply rely on traditional data. For accurate forecasting, it’s crucial to account for external factors such as weather, holidays, events, social media, and news as well as internal factors such as promotions, advertising, visual merchandising, etc.

Let ML Do the Heavy-lifting and Help You Decide What Factors are the Most Important

With a huge range of internal and external causal variables affecting sales, every store, channel, and category combination behaves differently. One of the biggest mistakes that grocers make is to force-fit models without understanding the nuances that are at play.

In a multivariate framework, it is very difficult and cumbersome to determine the importance of each factor manually. ML algorithms, however, can help to sift through data and determine the effect of each factor. This can then feed in as an input for the planner to generate granular and accurate forecasts.

Go the Extra Mile on Forecast Accuracy With an Ensemble of Algorithms

While forecasting sales of products, there is a slim chance that you will find a silver bullet algorithm that works for all products, locations, and situations. Therefore, champion grocers go the extra mile with an ensemble of algorithms that is customized based on the data. This ensures that grocers avoid over-fitting of models across product lines and achieve greater overall accuracy.

Adopt a Dynamic Approach to Fresh and Ambient Products With Business Objectives as Priority

From fresh goods wholesalers to grocery retailers, from high-end to price-driven supermarkets, convenience stores to cash-and-carry chains, it is clear that replenishment optimization teams walk a tightrope between spoilage costs and shelf presentation. This makes it important to get the balance right every time.

Error functions such as RMSE and MAD are powerful tools that can be used to select the best model by analyzing the prediction error. Such methods are indifferent to over-forecasting and under-forecasting. However, depending on where the product lies in the fresh to ambient spectrum, these functions can be tuned to treat over-forecasting and under-forecasting differently based on the business requirement and impact.

Tie Your Forecasting to Outcomes

What should be your forecasting accuracy? Should it be above 95%? Or 99%? The correct answer to this question is not so simple.

Most forecasting techniques aim to achieve the highest accuracy levels, giving very low importance to business outcomes. Tying your forecasting to outcomes such as reducing wastage, overstocking, or increasing availability has helped several leading retailers achieve great success even with forecasting accuracy as low as 70%.

Pro-actively Adapt to In-store Scenarios

While managing stocks at grocery stores, it is critically important for store managers to respond to what is happening on the shelf. For instance, a new product launch could lead to secondary effects on the demand for other products, which could range from an overstock situation in case of cannibalization to understock in case of multi-buy discounts. For true agile operations, business users should be able to swiftly identify and plan for such situations on a daily basis without the need of technical support.

Events that cause a secondary effect on other products:

  • Multi-buy discounts
  • Price change
  • Promotions
  • Advertising
  • Change in in-store display
  • Product launches and discontinuation
  • Macro-level scenarios such as weather, local events

Don’t Discount the Cannibalization Effect

The effect of promotions of products via price discounts, advertisements, display changes, etc. on the supply chain is one of the least studied topics but has huge implications. For example, the promotion of one product may have significant effects on the sales of other products that are not in promotion. Not accounting for this effect leads to suboptimal retail inventory optimization solution and ill effects like increase in spoilage or overstock. Promotion forecasts can go a long way to satisfy the increase in demand while mitigating the ill effects.

Sparse and Noisy Data Is the Norm, Not an Exception

With increased new product launches, fresh products, and increasingly complex channels, sparse and noisy data is a recurring theme across grocers worldwide. If you regularly face the roadblock of not having enough quality data for your planning needs, then it is time to look for a solution. Invest in a forecasting framework that uses data science techniques to deal with sparse and noisy data with ease.

Scalability Is Not Optional Anymore

Irrespective of you taking a top-down or a bottom-up approach to your demand planning, you will eventually have millions of demand forecasts at the SKU-store level. This planning is getting even more unmanageable due to changing consumer behavior and channel factors. Therefore, top grocers realize the importance of making sure that the system is scale-ready, both from a technical and user experience point of view.

In the ever-evolving, dynamic, and volatile grocery retail, your demand sensing and forecasting framework needs to be intelligent, agile, and scalable to be able to deal with the above-mentioned challenges. One such solution is ADA Global’s Forecast Right.

Forecast Right uses proprietary ML-based multivariate and algorithmic techniques to accurately and adaptively forecast demand. It is 100X faster and scalable than traditional forecasting solutions – 5-clicks is all it takes to generate 1000s of granular forecasts. Its proprietary AI provides tailor made feature engineering and model selection for demand forecasting and has a track record of improving forecast accuracy for over 90% of SKUs.

The output of a solution like Forecast Right can be plugged into various use cases. One such use case is replenishment planning solutions. Powered by Forecast Right, ADA Global’s Order Right generates accurate SKU-level order plans for even the most challenging categories – from fresh and seasonal to new and promoted products with ease. It does so by leveraging proprietary optimization algorithms that constantly monitor stock balance, sales and demand predictions while accounting for constraints such as shelf-life, lead-time, expiration date, minimum order quantity, minimum display stock, and automatic store replenishment constraints.

Also read: How Machine Learning Improves Retail Demand

Learn more about ADA Global’s Forecast Right and Order Right.

Table Of Contents
Account for Dynamic Demand Forces With Multivariate Forecasting
Let ML Do the Heavy-lifting and Help You Decide What Factors are the Most Important
Go the Extra Mile on Forecast Accuracy With an Ensemble of Algorithms
Adopt a Dynamic Approach to Fresh and Ambient Products With Business Objectives as Priority
Tie Your Forecasting to Outcomes
Pro-actively Adapt to In-store Scenarios
Don’t Discount the Cannibalization Effect
Sparse and Noisy Data Is the Norm, Not an Exception
Scalability Is Not Optional Anymore

How Algorithmic Testing Is Changing Marketers’ Experiments

Posted on by
Date: 22 Apr 2022
Digital Experience Personalization
Blogs

How Algorithmic Testing Is Changing Marketers’ Experiments

The world of marketing has become way more defined and data-driven than it used to be. Multivariate tests and A/B tests, analytics, statistical significance, allocation of traffic, tweaking variables, refreshing, optimizing, and repeating – are now an integral part of every marketer’s life.

Clearly, testing has a huge bearing on the performance of campaigns and is the difference between running an optimized campaign versus a poor-performing one. Testing tools providers have built entire product lines around these, and you will find that they are constantly urging marketers to “test everything” and rightly so.

The world of retail is now too complex to tackle with manual testing. Manually tackling different flows of data for every individual is obviously not feasible. Adding more people to the testing team isn’t the answer. At the end of the day, the volume of data is too large and the value-add may be sub-optimal.

This begs the question: How can your business then provide tailored shopping experiences to customers without having to create discrete experiences manually for segments?

ADA Global steps in here with a range of native testing tools built specifically to address such concerns. These include traditional A/B and MVT tests, but in addition also have full-fledged continuous algorithmic testing and optimization.

What Is Algorithmic Testing?

Taking A/B testing and experimentation a notch or two higher, algorithmic testing uses data science with a continuous, always-on testing environment for improving conversions on the go.

It’s a critical need if your customer journey culminates with a purchase and you want to test conversions, especially on cart pages.

What Is Different About Algorithmic Testing?

As opposed to manual testing, algorithmic testing allows one to continuously test every variable, i.e., every digital transaction, while focusing on revenue conversion and at the same time, self-optimize to achieve the desired result via AI/machine learning, adapting at scale for every individual.

Algorithmic testing is one of the foundation stones of hyper-personalization.

How Algorithmic Testing Works

This works in two stages via an AI decisioning engine, known as the Experience Optimizer that is unique to ADA Global. While considering the context, the Experience Optimizer tests and picks the best experience for every individual automatically.

One can choose from over 150 strategies that have been tried, tested, and refined over time with outcomes specific to retail. ADA Global’s Xen AI consists of these strategies, and they are built using an ensemble of algorithms – including statistical, machine learning, and deep learning ones, like natural language processing or NLP.

In real-time, multiple strategies are tested against each other by the Experience Optimizer to determine the winner for any interaction. While doing that, it considers metrics like RPS, RPV, and CTR. It also tries out new content or other viable recommendation strategies that are relevant to each customer or visitor.

In its exploration mode, which is completely customizable by the business, the Experience Optimizer makes sure that results never hit any kind of ceiling, and continue to propagate over time.

New content or strategies can also be added into the mix by clients, and the Experience Optimizer can choose when they could be used given a situation.

ADA Global’s native A/B and MVT tools can be used by marketers to test out specific content, placements, strategies, rules, and even the merchandising of category and browse pages. These tests can then be targeted and accordingly, traffic can be allocated towards specific segments or all visitors. As opposed to traditional A/B testing tools, algorithmic tests treat each visitor uniquely.

Testing of strategies; optimization of metrics for different areas of the funnel; and testing of other configuration changes, campaigns, or promotions can also be done by clients.

The insights dashboard can be used to monitor results, or clients can even use the Experience Browser for the same. Results across multiple metrics are automatically shown, even if those metrics are not included by the user in their test. A variety of filters, including, but not limited to segments can be used to view the results. Outlier filtering can be used to measure the impact of outliers.

As the future of experimentation, algorithmic testing will operate alongside your regular A/B testing tools. It’s not like one can replace the other.

Learn about all the ways we can help your business drive hyper-personalized marketing at scale.

Content Personalization: ‘Experience Designer’ for eCommerce

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Date: 20 Apr 2022
Digital Experience Personalization
Blogs

Content Personalization: ‘Experience Designer’ for eCommerce

Creating content can be a daunting task, even for digital marketing organizations. The sheer number of variations you need to curate in order to target specific segments of customers in a data-driven manner makes the whole exercise quite cumbersome. Well, help is at hand – we are talking about Experience Designer to help you kickstart personalization of your content.

If you’re familiar with our personalization engine, you may be aware of the Experience Browser (XB), which is pioneering transparency in AI decisioning. Working with XB, the Experience Designer leverages Xen AI engine in order to identify the appropriate targets automatically and subsequently uses your existing content to create relevant campaigns.

Without the hassle of carrying out full tests, marketers can iterate and therefore distribute real data to the ones who are actually delivering the experience. This acts like a bridge between the marketing and commerce teams. Let’s dig a bit deeper into how this works.

Auto-discovery of Behavioral Segments

Segment creation is the most important aspect when it comes to targeting customers and creating personalized content. Since digital commerce is quite complex and volatile, extracting actionable and timely insights from data becomes a challenge. Let alone the time spent on it, even your entire analytics budget could be spent, and you would still not be able to fulfill your objectives. Auto-discovery of behavioral segments is a new feature that we have added to eliminate manual effort in the process of segment creation.

Through this feature of auto-discovery, Xen AI-driven machine learning algorithms replace costly and inefficient manual analytics to find new and more interesting behavioral segments, while also providing a web-based visual tool for reviewing and acting upon the insights generated.

Using this feature, you can easily discover segments based on any kind of filter, be it brand, category, or product. Furthermore, once identified, it becomes all the more easier to target said segments with highly personalized cross-sell campaigns and offers. For example, you can discover customers that convert but with low spends.

So, how does this auto-discovery work?

Using metrics like conversion rate, average order value, revenue per visitor, and the like, auto-discovery can cluster audiences together. After that, such clusters are filtered. Clusters are excluded if they have less than 10 percent of the overall paying audience. Also excluded are the clusters that have less than 5 percent (minor) positive or negative metrics variation when compared with the average audience metrics.

Assuming average conversion metrics, the remaining clusters are used to calculate the potential revenue as additional revenue.

Benefits of the Experience Designer

If you have used XB, you will find the Experience Designer to be quite familiar. It is a similar web-based tool that sits atop the web page on your browser, like an overlay, or a HUD, if you can relate to FPS games.

The Experience Designer can help marketers create, execute, and edit content-based placements and campaigns directly from the live web page, without needing to navigate to another tool or requiring the intervention of the IT team.

For marketers who wish to test, modify, or launch campaigns on the go, this tool can be a boon. Apart from giving the freedom to create campaigns fast, this tool also helps marketers realize the complete potency of the content in their libraries.

Once the segments are defined through auto-discovery, segment builder, or via the segment import tool for externally created ones, you would need to define the campaign/s for these audiences.

Now that you know what sort of fine-tuning you can achieve with audiences, you will be pleased to learn that the same kind of intuitiveness is also offered when designing a campaign. You can drill down to narrower targeting via metrics that were earlier just an unusable tag. Campaign design is rather effortless, with filters that allow for more specific control on the segment including subsets of monthly new or returning visitors, abandoned carts, infrequent loyalty members, etc.

After that, you would need to choose the content that is applicable from your existing library. This is then used by the Xen AI engine to continuously work towards optimizing for conversion or click-through goals.

Last but not the least, you choose where the placement would be on the website. All it takes are a few clicks – no coding required. There is an approval process that needs to be followed though, so your experiments are not pushed to production by an erring click.

And that’s all there is to it!

The Experience Designer helps bring in commerce data to understand conversions from content, and that is where digital marketers benefit the most. Why choose from among content and commerce when you can have the cake and eat it too?

Learn more on how ADA Global can help you strengthen your content personalization strategy.

Table Of Contents
Auto-discovery of Behavioral Segments
Benefits of the Experience Designer

Why Hyperpersonalization Is Key to Winning Grocery Retail

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Date: 11 Apr 2022
Digital Experience Personalization
Blogs

Why Hyperpersonalization Is Key to Winning Grocery Retail

One of the most remarkable shifts we have witnessed in retail recently has to be the unprecedented growth in the grocery segment. According to a recent report from IGD, grocery is set to grow by 24% and generate an incremental $2.2 trillion in sales by 2024.

The key drivers for growth across both mature and emerging markets in North America/Europe and Asia respectively will still be online and convenience. As grocers race to grab a share of the increase in consumer spending, they must be mindful of the changes in consumer behavior, the importance of catering to a whole new set of first-time customers, and adapting quickly to evolving needs. This means grocers need a fresh and nimble AI-driven approach to delivering exceptional customer experiences. This alone will separate the winners from the also-rans.

For starters, this means grocers must look beyond traditional marketing methods that involve segmentation which is essentially grouping individuals into buckets called ‘segments’ based on a certain set of common predetermined criteria including interests, geographies, and demographics.

In the past, marketers would typically define segments such as ‘shoppers over the age of 45 who spend 40 GBP monthly in-store’ or ‘households that buy more than 10 organic items monthly’. This helped marketers better organize large amounts of consumer data and personalize communications to some extent. Clearly, it has its drawbacks – since every consumer is unique, no two shoppers have the same buying habits, tastes, or preferences and naturally, you end up with sub-optimal results.

Bottom line, it isn’t true Individualization or what we now call hyper-personalization – where every customer is delivered unique, tailored experiences. Make no mistake, consumers today expect grocers to deeply understand their needs, preferences, brand affinities, and purchase behavior in real-time and to engage them via their preferred channel.

In other words, consumers are looking for contextual relevance, and hyper-personalization delivers on that promise by creating a real-time behavioral profile for every visitor that is extensible to their household. This is achieved by a deep learning framework, a real-time customer data platform (CDP), a full suite of experience personalization, and personalized marketing orchestration capabilities that afford retail grocers the benefits of precision marketing and scale.

Clearly, hyper-personalization is the way forward, but let’s look at what this means from a consumer’s standpoint. In a nutshell, it translates into three things – Recognize Me, Understand Me, and Inspire Me in real-time.

Recognize Me: Know your shoppers. What if you knew your customer shops for a family with two children during the first week of every month, and 30% of his spend is on frozen food. A small portion of purchases includes vegan products, indicating a family member is a vegan. Legumes, dairy products, and dark chocolate bars are regular buys. The customer also buys vitamin supplements, fabric softeners, and disposable plates every alternate month. With this knowledge, you will be able to surface personalized product recommendations that drive better conversions.

Understand Me: Another important ingredient of hyper-personalization is knowing the Why behind the purchase. It can provide surprising insights into the shopper’s context and enable you to tailor the shopping experience. If you know that a customer often purchases avocados and is into healthy foods, you could recommend products that complement it (Mexican salsa for example).

A change in buying patterns or habits may be indicative of moving to a certain variety of food (gluten-free) or an addition of a pet. You may also discover that this customer engages with gamification content and often redeems points to get discounts or you may encounter a customer that constantly compares prices for select staples online and buys in-store. This level of deeper understanding allows you to adapt instantly to changes in customer behavior and extend the best, most relevant offers.

Inspire Me: The more you know your customers, the better your ability to personalize and drive conversions. When you understand affinities, preferences, and behaviors deeply, you can surprise and delight them with the best in-the-moment recommendations that drive higher engagement and top-class digital experiences. These can range from recipes to one-click ingredient purchases or recommendations based on the weather, unique bundled offers, or surprise customers with new product alternatives. You can bring insights from your in-store purchases to influence the online shopping experience.

In summary, massive opportunity beckons grocers. So why not look beyond the mundane automated replenishment to deliver a rich user experience that keeps customers coming back? Hyper-personalization has all the answers to convert routine buying into delightful shopping experiences. Make grocery shopping joyous again.

Check out our Guide to Omnichannel Personalization to learn how you can integrate your online and offline channels to deliver a more holistic customer experience.

eCommerce Spikes Galore – How’s Your Personalization Engine Faring?

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Date: 29 Mar 2022
Digital Experience Personalization
Blogs

eCommerce Spikes Galore – How’s Your Personalization Engine Faring?

Over the past 24 months or so, eCommerce across online grocery, consumer electronics, home appliances, fashion, beauty and wellness to home entertainment purchases including streaming services and beverages like wine, beer, and liquor has seen sharp growth. An eMarketer report predicts that the global retail eCommerce sales will reach a value of $6.17 trillion by 2023, accounting for 22.3% of the total retail sales, up from 13.8% in 2019.

ADA Global has been observing sharp spikes in traffic that resemble days around Thanksgiving. In other words, almost every other day seems to be a Black Friday.

So, is your real-time personalization provider equipped to handle this?

In the recent past, there have been many new personalization providers that claim to offer personalization. Their offers include simplistic recommendation engines, A/B testing tools, and even search engines that tout the ‘wisdom of the crowd’ functionality. One of the things that is easily overlooked is their ability to smoothly manage these spikes in traffic, for which they are wholly dependent on third-party cloud service providers.

With consumers expecting top-notch real-time personalized experiences, the associated workload is difficult to predict and has a direct impact on variable costs. Companies that are ill-equipped to handle this at times could compromise ecommerce personalization solutions techniques to limit their exposure.

To cite an example, one of our leading competitors was so bogged down with the cost spikes from their cloud provider, they decided to switch off some of the personalization features for more than 50% of their customers! Just think about the repercussions of that step.

Imagine a scenario where you are relying on the personalization provider to drive more conversions by leveraging the full extent of their expertise, especially when you are experiencing a big influx of traffic. What if at precisely this time, your shoppers start seeing non-relevant or generic recommendations at the time when you need to display products that best match their preferences and balance them with popular new products or present the best-bundled offers. In other words, you end up losing precious sales and worse still not have those shoppers return, even though you pay a premium for it.

The only way for these software companies to meet their operating costs is by passing on these costs to their clients on a monthly recurring basis. If you are an ecommerce personalization platform in an intensely competitive environment, the last thing you need is escalating costs that eat into your margins.

The advantage of working with a provider like ADA Global is that the foundational platform sits on 13 geographically discrete and spread-out data centers and offers you a response time that is the best in the industry.

Furthermore, ADA Global’s IT and Ops teams closely monitor the Internet and shopping traffic, and can smoothly scale capacity on-demand, whether it is up or down. This has allowed ADA Global to consistently provide 100% uptime during festive and high-traffic periods with a great response time and perfectly adapt to seasonal changes for the past 14 years.

So, before you sign your next contract or even renew it, we’d urge you to pay special attention to the fine print on variable costs. Importantly, choose a personalization provider that has proven credentials in supporting companies operating in high-growth environments and understands the nuances well.

With more than 400 top retailers and brands on the ADA Global platform, we know a thing or two about delivering the best real time customer engagement no matter what the conditions. You can

Learn more on how we help retailers deliver personalization at scale.

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