Online Retail Today
Four Ways to Think Store First with Mobile

Four Ways To Think “Store First” With Mobile

To no one’s surprise, mobile is merchants’ top priority for 2017 — reflecting the runaway growth of mobile shopping activity. Amidst a bevy of potential priorities, merchants can reap the most benefit from mobile initiatives by adopting a “store-first” mentality that emphasizes connecting shoppers with physical retail outlets.

There’s no doubt that mobile shopping activity is soaring. More than two-thirds of all online shopping activity occurs on mobile devices, and mobile sales are growing exponentially: in the fourth quarter of 2016, for example, year over year mCommerce sales shot up 45% compared with 2015, and accounted for almost 21% of all online sales, according to measurement firm comScore.

Overall, 31% of U.S. consumers now say they’ve made purchases on mobile devices — a 24% increase since 2014, technology researcher Forrester found. It’s not surprising, then, that merchants participating Forrester’s annual retailer survey ranked  mobile at the top of the list of strategic priorities for the fourth year in a row, with an emphasis on achieving ROI through continuing mobile revenue growth.

But while mobile commerce sales are increasing, the $60 billion total is still just 1.3% of all retail sales, and 15% of all online sales. The much larger, if more difficult to measure, benefit of mobile comes from its influences over sales across touchpoints, and especially in stores, which Forrester estimates at more than a trillion dollars — or a whopping 31% of all retail sales and counting.

Given that fully 40% of shoppers now use multiple touchpoints when shopping, mobile’s sphere of influence should come as no surprise – but most merchants’ efforts to use their mobile sites for engagement and research, not just as mini-eCommerce sites, still leave much to be desired.

To encourage shoppers to use mobile devices to connect with physical outlets — and drive physical store sales — merchants should adopt not just a “mobile-first” approach, but a “store-first” mentality within mobile experiences that goes well beyond a store locator to prioritize potential connections to in-person experiences.

In some cases, that may mean building an app that enables immersion in the merchant’s branded experience and facilitates connection to store services. But with or without an app, merchants can go “store-first” by:

  1. Find ways to measure mobile influence, not just sales. Merchants should go beyond tracking mobile sales and conversion rates to capture more ineffable connections, such as usage of in-store wifi, usage of online tools to set in-store appointments, and downloads via store-specific QR codes or other custom URLs. Social media monitoring should include tracking of store check-ins and photos tagged with store locations.
  2. Grant universal inventory visibility to mobile shoppers. By now, the evidence is overwhelming that shoppers desire flexibility when it comes to order fulfillment. More than three quarters of shoppers used “buy online, pick up in store” (BOPIS) services in the past year, Kibo found — and the lack of such inventory transparency can actually hurt sales: 80% of shoppers say they’re less inclined to visit stores without the ability to see whether desired products are in-stock locally. While merchants are increasingly making this information available — some 53% already offer in-store pickup services, according to Kibo’s “In-Store Meets Online” merchant survey — deployment to mobile channels is uneven, with a few hapless merchants even offering BOPIS to shoppers using desktop browsers, but not those accessing mobile Web sites.Kibo merchant Helzberg Diamonds prioritizes connections to physical outlets by offering not only a store pickup option for orders transacted via the mobile Web site, but an appointment feature that allows shoppers leery of buying big-ticket items online (or especially on their phones).
  3. Prioritize product content for researchers. Fully 94% of shoppers now conduct research online prior to a store visitaccording to Kibo’s latest consumer trends report. And given that accessing product information and pricing are the top three research activities shoppers undertake on mobile devices, merchants should spotlight customer reviews, product how-to videos, product comparison tools, and sizing and fit calculators within the mobile environment. By enabling research — regardless of whether shoppers choose totap “buy” on their mobile screens — merchants demonstrate an authentic desire to help shoppers find solutions that fit their needs.
  4. Factor stores into marketing campaigns. When it comes to mobile marketing, store locations should be front and center. Mobile search campaigns should showcase nearby physical outlets, SMS campaigns should be tailored to regional conditions and local outlets, and even email campaigns — two-thirds of which are now opened on mobile devices — can at least include prominent links to storelocators.Kibo merchant Party City routinely includes in email campaigns links to coupons for in-store use alongside online promo codes, as in this recent promotion, which also features a highlighted link to the store locator.

How are you using mobile to connect shoppers with store shopping experiences?


Targeting vs Personalization

The Difference Between Targeting And Personalization

The words targeting and personalization have become interchangeable in many lexicons. Unfortunately this is a dangerous habit for those in the retail space, as they have separate definitions and uses. This post will detail the differences and why marketers would want to start making the distinction between the two words.

Targeting is about marketer needs.

Personalization is about consumer needs.


Both techniques can increase revenue but they come at it from different angles. 

Segmentation and targeting

Targeting is about marketer needs and insights. Rewind to the one size fits all experience. The site shows the same offers, content and products to everyone. With targeting, marketers and merchandisers take insight from their experience and attempt to show relevant offers, content, and products to visitors. They use a variety of techniques, but the most common involves segments and rules. Most marketers have a few buckets that they group customers into and this is as much for the marketer as anything else – new visitors versus loyal shoppers, soccer moms versus football dads, etc. The marketers believe that these groups buy different stuff or react to different content or promotions. In addition, these segments make sense to the marketers and help them organize their thoughts and content around key groups. For example, a marketer wants to provide an incentive for first time shoppers to come back again. So, they set up promotions to target visitors in this segment. This is a great strategy. It allows the marketer or merchant to control what the visitor sees and hopefully allows them to optimize based on business needs.


Personalization happens in real-time

Unlike targeting, personalization focuses on consumer needs. What offers, content, or products will a particular visitor find most relevant based on their current needs? To create a relevant, personal experience for each visitor, a personalization engine soaks in all kinds of information about the visitor – where they came from, where they are located now, where they live, what they searched on, what page are they looking at right now, what they have bought before, what segments are they a member of, etc. All this information combined with powerful machine learning algorithms tells a personalization engine what content or products they are most likely to be interested in right now. As the visitor moves through the site, their intent can change. At first they may be interested in a new sweater and then their interest shifts to new jeans. The personalization engine goes along with them and continuously personalizes the content and products to best meet their needs given all the data that is available. Personalization at this level is not something that a marketer or merchant can figure out ahead of time.


Targeting and personalization working together

Targeting and personalization can actually work together quite effectively. A modern personalization engine actually uses the marketers segments as one of its inputs. The domain expertise used to generate the segments can be quite useful. If segments prove to have different interests, then the personalization engine should provide different content to each segment. In addition, domain experts may want to promote or target certain items within personalized areas of the site based on their business needs. For example, marketers may want to target a promotion at members of a loyalty program or merchandisers may want to make sure that the combinations of items are always shown together. Either way, the idea is to show the most relevant and hopefully valuable offers, content, and products to consumers that will encourage them to buy more, and buy more often.

When marketers and merchants think of the offers or products they want to promote, they are thinking about targeting. When retailers let visitors drive the decision making with their data and based on their intent in real-time, they are personalizing. This distinction is key to optimize your site for the best customer experience and ROI. Discover more about what real-time personalization can do for your business.

10 Mobile Metrics to Track Now Before the Holidays

7 Ways To Measure Omnichannel Performance

When it comes to eCommerce analytics, the buzzwords are by now well-worn. Say a merchant needs to develop a “360-degree view of the customer,” and she’ll likely respond, “Yeah, yeah. Tell me about it!”

As many have discovered, while the goal of understanding consumer behavior across touchpoints is worthwhile, the means to achieve that goal remain elusive. Despite a burgeoning array of big-data tools and social listening platforms feeding ever-more-complex recommendation algorithms, merchants are still struggling to derive the meaningful insights that lead to more satisfactory experiences for shoppers — and, in turn, earn more revenues and loyalty.

In 2016, just 41% of digital marketers participating in an Adobe/eConsultancy survey agreed that they have the staff and technical capabilities in place to collect the right data and put it to good use. And shoppers report that clumsy efforts to wield data erode confidence in merchant brands: 23% of shoppers said they were put off by “incorrect data” in personalized messages, while one in five reported receiving promotions for products they’d already purchased.

And yet, it’s clear that merchants must forge ahead in their quest to better understand shoppers’ needs regardless of where and how brand interactions occur. Shoppers no longer distinguish between siloed channels when searching for products and services online: for example, 43% of consumers say they favor retailers offering consistent service online and offline, and overall, more than half say it’s important for merchants to recognize them across touchpoints and tailor content and offers accordingly.  Merchants must adapt their organizations — and metrics — to match these expectations.

Furthermore, better tracking across touchpoints can help justify investment in online initiatives that don’t necessarily result in direct online sales. After all, fully 51% of all retail sales are set to be influenced by the Web this year — dwarfing the 11.8% of revenues directly generated by online purchases.

For these reasons, merchants would do well to focus performance measurement initiatives on uncovering the metrics that illuminate and quantify the Web’s broad sphere of influence over offline shopping activities. Among the tactics to consider:


Scrutinize attribution models closely. It’s tempting to set percentages to apportion credit for online revenues among the marketing touchpoints that preceded a sale as a means of capturing indirect influence — and indeed, such models are an improvement on “last-touch” attribution that credits only the source that directly referred the sale. But modeling is still far from perfect, as biases among target audiences can skew results. Merchants would do well to scrutinize attribution results closely, adjust as necessary, and use the models as just one factor when determining where to invest marketing dollars.

Map and market to store influence zones. Marrying online behavioral data with geographic input relative to store locations has the potential to wield significant insights, as merchants can track usage of features such as “buy online, pick up in-store,” and “reserve online, buy in-store” and determine the size of the zone influenced by store locations as a result.

While it’s tempting to allocate revenue based on this modeling, such an exercise is likely to prove a zero-sum game: after all, just as store outlets deserve credit for handling fulfillment activities for online purchases, the eCommerce site can help spur additional store revenues when shoppers visit to pick up items bought on the Web. Rather than split hairs over which silo gets how many dollars, merchants would do better to use store-zone performance to influence geo-targeted marketing offers and messaging.

Understand in-store usage of online assets. As discussed previously, shoppers rely on their mobile devices to conduct in-store research — so merchants would do well to identify and report on this distinct activity in order to justify investment in technological upgrades such as in-store wi fi, tablet apps for sales associates, informational kiosks, location-specific downloads and coupon offers, and even beacons and geo-fencing tools.

Standardize and measure customer service across touchpoints. Traditional call center metrics must be modernized to account for increasing usage of live chat and social media for customer service, so that successful practices can be identified and shared across the organization. Usage of self-service customer service tools, such as FAQs and Q and A features, should also be factored in.

Recognize loyalists and advocates. To uncover usage patterns among repeat customers — and encourage more buyers to return — merchants should track redemption of loyalty points or perks online versus offline and on mobile devices. Similarly, referral programs that reward (and track) both the referrer and the recipient of the referral can help merchants identify and serve segments of customers who deserve tailored VIP treatment.

Invest in identity matching tools and test continually.  While the much-vaunted “360-degree view of the customer” remains elusive, merchants should do their utmost to unite disparate data sets and stay at the vanguard of innovation when it comes to identity matching. Continual testing, analysis, and adjustment to third-party tools will help put the holy grail within reach.

Add omnichannel metrics to routine analytics reporting. If they haven’t already, merchants should expand their performance dashboard to include metrics that go well beyond conversion rate and revenue. Among the data points that should be collected and segmented by screen size and location:

  • Use of inventory lookup via the eCommerce/mCommerce site and app
  • Use of “buy online, pick-up in store” and other in-store fulfillment options
  • Online registrations for in-store events, personal shopper appointments, and the like
  • Browsing, cart additions, and purchases occurring within store locations
  • Online orders placed via employee point-of-sale apps or tool in stores
  • Use of the “save cart” feature to move between digital touchpoints
  • Use of the “print cart” feature
  • Use of “like” and “share” buttons from product pages
  • Email signups from the Web site, social media, and app
  • Mobile app downloads
  • Usage of stored payment data within apps or the mobile site
  • Usage of alternative payment methods


How are you measuring performance to reflect today’s omnichannel reality?


retail trends expected by consumers

5 Retail Trends Now Expected by Consumers


Today’s shoppers are mobile. They are deeply involved on social networks. They are constantly connected on and distracted by multiple screens, while their daily lives at home, at work and at school are constantly on-the-go.

This is a radically-different consumer environment than a decade ago, before Facebook, Twitter, iPhones and Androids flooded the landscape. These days, you won’t find customers of all ages easy to reach through network television advertising. Shoppers are no longer tied to their desktops or tethered to their local brick-and-mortar store. Consumers want what they want, when they want it — and typically they want it now.

These changes are rocking retail to the core. So, it’s no surprise that forward-thinking retailers are now working overtime to keep up with their rapidly-changing customers. Those that do this well will be the ones that succeed and grow with exciting and expanded opportunities.

Those that don’t? Well, they may be left in the dust. Here are five top trends that are shape-shifting retail right now:


1. Retail customers want it their way — personalized and customized.

No longer are shoppers willing to settle for what everyone else has and what everyone else gets. Instead, customers expect increasingly personalized communications — through ads, online recommendations and email marketing — as well as customized purchase options. For example, online retailer Stitch Fix is a service that offers personal styling for affordable prices. Sportswear retailer PUMA provides a wealth of customized jersey options. And retailers such as Pepe Jeans are offering customized options and virtual shopping services.



2. A seamless customer experience is becoming retail table-stakes.

Shoppers no longer distinguish between channels when it comes to their purchase process. Whether it is on mobile, in-store or online, they want their experience to be equally quick, easy and integrated. This is a tough challenge for retailers, however, who have to overcome big supply chain and technological obstacles to make this expectation a reality. In fact, according to Accenture, there are clearly gaps between consumer expectations and the ability of retailers to deliver what customers want — such as anytime, anywhere fulfillment and a consistent cross-device experience.

3. Social is becoming about shopping — not just sharing.

Retailers have long used social networks such as Facebook and Twitter to share information about new products, sales and to improve communication with customers. Now, however, social is becoming about actual purchases, not just sharing. EMarketer recently found that “Buy” buttons are becoming more commonplace, with all the major social networks implementing them in some way. While so far fewer than half of retailers are using the “Buy” buttons, it’s clear that they are intrigued — and experimenting with them on Facebook, Twitter, Instagram and Pinterest, among others.

4. Shoppers crave instant gratification.

Companies such as Uber, GrubHub and Amazon have entirely changed the shopping game by offering customers new ways to get what they want, faster. Want a car at the corner in a couple of minutes, rather than hailing a taxi? No problem. Want to order your meal online and have it delivered pronto? Sure. How about delivery of anything, anywhere, at your fingertips? Of course. As consumer thinking about service changes, retailers of all stripes are feeling the pressure to step up and are working to embrace strategies and technologies that help them, literally, deliver the goods.

5. Consumers want to shop easily on mobile.

Mobile commerce is gaining serious momentum: According to Internet Retailer’s 2016 Mobile 500 study, in 2015 US mobile commerce sales grew to 30% of total e-commerce sales, or a whopping $104 billion. Consumers want their mobile shopping journey to be as straightforward as it is on the desktop or in the brick-and-mortar store — with responsive sites, speedy page-loading and easy product discoverability. However, so far retailers have not been able to rise to that challenge when it comes to actually getting to the path-to-purchase finish line on mobile. In fact, only 42 percent of shoppers found it easy to complete a purchase using a mobile device, according to Accenture.

Does Your Mobile Commerce Strategy Demand an App?

It goes almost without saying that the mobile commerce revolution is underway. Billions of smartphones connect users to exactly what they want instantly. Many would argue we are post revolution and just living in the new reality of mobile.

The growth of mobile is powered in part by the highly contextual nature of the smartphone. Many in the space refer to these interactions as ‘mobile moments.’ These quick encounters with the smartphone add value to everyday interactions. This behavior can be leveraged by retailers if they are ready with the right apps and mobile optimized sites.

How does a retailer make sure their mobile strategy hits the right chord with customers?

The first thing to understand is that there are varying degrees of mobile strategy and the strategy selected depends on the customer base.

Here are the options:

  1. Just use a mobile site: Don’t bother with an app. This option is great for retailers who’s users aren’t inclined to use an app or they don’t see the value-add of keeping and using an app on their mobile device. As long as sites are responsive, this is the easiest of the options.
  2. Recreate the mobile site in an app: This is probably the most common type of app out there, but the value-add is unclear. Consumers see right through a mirror copy of the mobile site. With space at a premium on mobile devices, each app has to earn its place.
  3. Create a new app experience: The most potentially rewarding, but also the most technically demanding and difficult to implement. This is a great option for retailers who want to really engage their customers and give them a great brand experience, typically by offering a service not found on the mobile site.

While mulling over the options, consider this advice: CEO and co-founder of mobile agency 64 Labs, John Duncan, advocates for a hybrid approach of Nos. 2 and 3 that allows retailers and branded manufacturers to use elements of the website where appropriate, but also offer the unique things an app can do. This avoids reinventing the wheel but also delivers app-specific benefits.

And what are the benefits of a mobile app anyway?

The true power of the phone is its ecosystem, as explained below:

  • Social apps: Customers are very likely to be already logged into social apps such as Instagram or Facebook on their phones. With one tap, they can share items from an app, whereas there may be multiple steps in mobile web or desktop environments.
  • Mobile wallets: Integrating mobile wallets lowers transactional friction, as customers don’t have to reach for their credit card to make a purchase. Also, integration is less of a technical hurdle because of its native functionality.
  • Location services: For omnichannel retailers, location awareness can add context to in-app interactions. Think of the power of an app interaction that knows your customer is in one of your stores. It can also be a powerful tool for engagement when combined with push notifications to create beacons.

At the end of the day, the most important part of an app strategy is knowing the customer base, the same as the retail core competency. If an app is right for the users it can be a rewarding venture. These loyal customers are the greatest advocates, and should be used to iterate on your app and make it better.

Making design and development choices that make an app easier to maintain should also be a consideration. Apparel retailer Bluefly chose Kibo’s native mobile app for their app experience because they could change the content in the app using the same CMS as the rest of their site.

Finally, an app is a long-term commitment. Duncan notes, “Having an app is like having a pet. You can’t get one and forget about it.”

What do you think? App or no app for your business?

Why Smart Shoppers Love Your Website, But Hate Your Dealer Locator, Product Recommenders

The Dirty Secrets of Product Recommenders

Product recommendations are a common feature of many eCommerce websites and commerce systems. While simple product recommenders are an important technology, the recommendations themselves are not related to the individual customer. People often think product recommenders and personalization engines are one and the same. There are very distinct differences between the two and how they work. Let’s take a look at few of the ways that simple product recommenders masquerade as personalization systems.

First, some background on the topic. The basic technology behind many product-to-product recommendations is collaborative filtering. This technique, made famous by Amazon, uses an aggregate of browsing behaviors of shoppers visiting the site to figure out things like “people who viewed this also viewed these other items” or “people who bought this item also bought these other items.” The result? The website can showcase a limited set of items that are likely to be interesting to a customer who is looking at a particular item or has placed items in their cart.

It may seem obvious, but simple product-to-product recommendations are not personalized to the individual customer. Every customer that comes to a particular product page will see the same items on that page. The product recommendations are contextual to the product on the page or in the cart. This is not a bad thing. The customer’s intent is usually aligned with the products they are looking at, so this is a good and simple way to show items a customer is likely to be interested in. This is why these simple product-to-product recommenders are so successful and why these techniques are some of the key building blocks of modern personalization engines.

But that is all product recommenders are: A first step. Below are four secrets about how product recommenders can not offer one-to-one individualized experiences that today’s demanding shoppers have come to expect.

Secret No. 1:
Simple product-to-product recommendations seem personalized because they rely on recently viewed or purchased items from a customer’s history, then use that item to make “personalized” recommendations. Again, this is a great technique when you have tons of data like Amazon. This technique is frequently used for homepage recommendations to get people re-engaged into their shopping journey or in emails to highlight products that are related to a recent purchase. This technique does NOT take into account anything about the customer; including their preferences, location or other attributes to refine recommended items to align with the individual customer.

Secret No. 2:
Simple product-to-product recommenders are not that hard to develop. Today’s computer science courses teach about these recommender systems. The tools and collaborative filtering techniques are widely available as open source. For this reason, many eCommerce platforms and marketing clouds (and even commerce brands) have built simple product recommenders of their own. These vendors then sell the simple recommendations as a personalization engine. Unfortunately, their clients are looking for a feature and assume that what the vendor has to offer will be as good as any other, which is not the case.

Secret No. 3:
Simple product-to-product recommenders can give you pretty bad results especially on low traffic areas. Many marketers assume that they can deploy automated product recommendations and that they will perform equally well on all pages on their site. For sites with low traffic, large catalogs or lots of long tail products, there can be serious issues with techniques that rely only on large volumes of browsing behavior alone. Not everyone can be Amazon. If not enough people browse or buy a product within the time that you are computing your models, there will be a very weak or no affinity between products. This is dangerous. While the system comes back with results, the items displayed are probably going to look at best silly or worst inappropriate. You can see this on many sites that have implemented simple recommendations as you browse around the site. You will see things that just don’t make sense and this is one reason why.

Secret No. 4:
Simple product-to-product recommenders have a limitation in that they can only provide recommendations for a single product at a time. For example in the shopping cart, when a customer adds more than one item, these product recommenders will simply make recommendations on the most recently added item rather than on the set of items. Therefore, the recommendations can be hit or miss. If the customer is adding a single item or random items, this might work. But if the customer is building an outfit, the recommender can miss the mark by only looking at the latest item instead of the entire group of items. Again, the recommender would benefit from knowing more about the customer like what types of items they prefer, what brands they like or what items they already have in their wardrobe. But being a simple product-to-product recommender, this information is not part of the equation.

Moving Beyond Basic Product Recommendation
Product-to-product recommenders are a critical stepping stone for sophisticated modern personalization engines. What these simple recommenders lack is a true connection to the individual customer. Today’s personalization engines take recommendations to the next level by combing each customer’s browsing and purchase history to understand their preferences for things like categories, brands, colors, fabrics or styles. Personalization engines also examine customer similarity to determine items that particular customer might like based on what other people in their segments or location are interested in. This technique, combined with an understanding of product-to-product affinity, provides the true one-to-one personalized recommendations that many retailers and customer have been hoping for.

To learn more about next generation personalization, and how it can benefit your business (and delight your customers), please download this eBook: The Ultimate Guide to Personalization

Online Purchases optimize your ecommerce

Why the “Real-Time” in RTI is so Important

Welcome to the Kibo blog series about Real-Time Individualization. We invite you to learn about the difference between individualization and personalization in the first of our blog series here.

What is Real-Time Individualization?
The addition of real-time technologies to drive more individualized experiences is a key part of the modern commerce experience. Customers expect websites and mobile apps to understand them and their environment and react to their inputs in real time. So, what does it mean to be real-time in the context of individualization? Here is what you need to know:

Responding to a real-time request for content
The most basic aspect of a real-time system is being able to respond to a request from another system in real time (i.e., such that that the response data can be used to satisfy a need of the other system during the rendering of the current interaction with the customer). This aspect of real-time has been around forever. The systems we built in the early to mid-two thousands could respond in half a second, which was good enough for the time. With newer technologies, we have just pushed down this real-time response window to a few milliseconds.

The latest and greatest customer data
Probably the most touted aspect of modern real-time systems is moving customer data in real time. In older systems, the customer data is updated in large batches every few hours or every day. This means that marketers and websites have to wait to take advantage of new customer information. With real-time customer data, systems can start using new customer data right away and react to the needs of the consumer immediately.

Machine learning models
The final aspect of any true real-time system, which is often overlooked, is the ability to update models with the latest data. This requires a real-time modeling engine powered by technologies like Spark streaming. With these technologies, the machine learning models use micro-batches of data to update their relationships. Instead of waiting for hours and possibly millions of data events, these systems can use minutes or even seconds of data to update their understanding of the real-time trends.

With advances in technology, the individualized customer shopping experience we have all been waiting for is finally here. The gem that is hidden-in-plain-sight is the power of real-time to deliver these one-to-one experiences.

seamless customer experiences

eCommerce Personalization During the Holidays

It is true that personalization has historically caused consumers to be chased by promotions and offers that are not relevant to them anymore, especially during the holidays when they are purchasing for family and friends.  This is a perfect example of where the definition of personalization is off — the issue described above is a tell-tale sign of the pitfalls caused by static segmentation – hard, fast rules created by the business to identify a user based on past behavior and experience to put them in a finite bucket of preferences. For example, you browsed or you bought product X, therefore from now on, you must always like products similar to product X — such as same price, genre, or category. 
True personalization is advancing with real-time individualization technology that responds to the consumer’s current behaviors and leverages past behaviors for refinement (such as geography, membership rewards, or past purchases).  We call this dynamic segmentation — the ability to move a user across many segments in one session based on current intent. The algorithms used in this approach are becoming much smarter and enables retailers to create 1:1 offers and recommendations in reaction to real-time behaviors.  For example, I may be shopping for my daughter for the first five minutes and my son the second five minutes — dynamic segmentation will individualize the experience for me in either case — matching me with the other segments of users in real time and personalizing my experience to be mirrored with the most successful conversion paths of those users (learn more about dynamic segmentation in this mention in Forbes). Algorithms used by dynamic segmentation change rapidly as they are constantly adapting to changing consumer behavior. To successfully achieve this level of personalization, retailers must invest in the appropriate technology. 
Real-time individualization can be a great benefit to consumers and retailers during the holidays. While a shopper is viewing potential gift items, they could be shown additional recommendations on page or in cart, or emailed a discount for the item they are considering.  If the item goes out of stock, the shopper will no longer receive those recommendations or promotions. All of this provides a helpful, relevant shopping experience, and prevents disappointment if a shopper goes to order an item they were just recommended, only to find it’s no longer in stock. The beauty of real-time individualization comes when the shopper shifts gears and is done with their shopping list. They do not have to be faced with months of offers for toys that were a one-time holiday purchase. Algorithms immediately update and begin producing recommendations and promotions for new items that are of current interest to the shopper. 

Our advice to retailers is to take a hard look at your static segmentation models.  If they are too confined and are not increasing your conversations rates or increase your average order values, then turn them off and look at individualizing your personalization efforts. 

Blog contribution by Bill Hustad, 
GM of Personalization at Kibo