Online Retail Today
Holiday Season Customer Service

Tips For The Best Customer Service This Holiday

As the holiday season revs up, many sellers are focused on their promotional calendars, hoping that savvy discounting strategies and big wins on key dates such as Black Friday will bring success. But just as important, though often overlooked, is customer service — a core component of the shopping experience that can make or break brand loyalty.

Given that few brands can compete on price with discounting juggernauts such as Amazon, customer service is increasingly a key differentiator for small- to mid-sized merchants. Those who promote and deliver on the promise of stellar service — from helpful pre-purchase interactions to on-time order delivery to convenient returns — stand to retain customers and earn word-of-mouth buzz. Those who’ve had positive prior experiences spend an average of 140% more than those who ran into customer service roadblocks, according to the Harvard Business Review. And thanks to social media, the potential downside to poor service is significant: Fully 46% of consumers — and 56% of Millennials — have called out brands on social networks for poor customer service, according to Sprout Social.

Customer service is so important that Kibo’s customer success team devoted an entire holiday readiness webinar to the topic. Among the priorities they identified:

 

Spotlight holiday shipping deadlines now.

Given that some 40% of shoppers have begun their holiday gift-buying in October or earlier, according to the National Retail Federation, merchants should stack the deck of free shipping promotions in favor of early purchasing and spotlighting those specials now, along with shipping timelines for the pre-season as well as the peak period after November. To further stimulate planning and early purchasing, merchants should message holiday cutoff dates early and often via multiple touchpoints — from triggered email messaging to social media.

And “early” doesn’t just apply to the holiday calendar — it’s also relevant for the path to purchase. Shipping processes and timeframes should be tailored to the item level and displayed on the product detail page as well as in the cart and checkout. For perishables, large items, and other merchandise requiring special handling, information should be tailored and specially flagged. Overall shipping cutoff dates and holiday delivery services can even be promoted via banners from the home page and category pages, as well as on advertising landing pages.

Vet BOPIS order process end to end.

Speaking of order fulfillment, merchants should cater to the 79% of shoppers who use Buy Online, Pickup in Store, aka BOPIS, services as a speedy and free way to take possession of their goods. For starters, sellers should list BOPIS options alongside home delivery options wherever shipping information is displayed — and highlight BOPIS in particular, especially as delivery cutoff dates speed past.

In addition, merchants should vet their BOPIS experience thoroughly in advance of peak crowds to make sure messaging is accurate and consistent, from the first inventory lookup option on the product detail page through to the in-store signage directing shoppers to the pickup desk. While it’s late for major technical overhauls, sellers can tweak verbiage to clarify the process, adjust in-store merchandise displays to tempt pickup customers to make additional purchases, and find ways to proactively message about any potentially confusing hurdles that might lead to dissatisfaction or delays.

(Merchants who don’t offer BOPIS should move it to the top of the 2018 priority list — and consider Kibo’s industry-leading omnichannel solution to create a seamless customer experience.)

Make returns a win-win.

Given the uncertainties of ordering items online without touching and trying them, it’s no surprise that fully 30% of eCommerce customers go on to return purchases — a percentage that jumps during and after the holidays, when gift returns are rampant.

So given how prevalent returns are, they needn’t be the end of the customer relationship; rather, they can be an opportunity — if the process is painless. Indeed, fully 92% of shoppers returning items say they’re willing to consider purchasing from the same merchant again based on the ease of the returns experience. Shoppers who can return online orders in-store may reward that convenience immediately: 70% make an additional purchase during their visit.

During the holiday season, merchants should highlight convenient return policies — from free return shipping to in-store returns for items bought online. And while it may seem self-defeating to promote returns even before orders are placed, such transparent messaging signals a commitment to service that can boost trust and brand loyalty. Proactive messaging about returns via social media and on the eCommerce site can reassure harried holiday shoppers that their gift picks won’t go to waste if they’re not a perfect match.

Use chatbots sparingly and transparently.

Shoppers have ever-heightening expectations for swift customer service, especially when it comes to social media. Upwards of 40% of shoppers who use social media to reach brands expect a response within an hour, according to research from The Social Habit.

With this need for speed, it’s tempting for merchants to turn to artificial intelligence and chatbots to handle customer service complaints instantaneously. But chatbots that merchants attempt to pass off as human rarely succeed, and can alienate shoppers with generic or irrelevant responses that complicate rather than speed resolution of their requests.

To ensure both speed and satisfaction, merchants should triage incoming requests and route complex questions to live humans; chatbot usage should be limited to use cases where responses are clear-cut. Whatever chatbot interactions do occur should be clearly labeled as such, and easy access to live human help — via a link or toll-free phone number — should be readily available. Given that 90% of support requests are for order status updates — a task easily handled initially by chatbots — sellers can still realize cost savings in the call center even if that’s the sole service topic they delegate to AI-driven systems.  

To help slow the tide of incoming requests in the first place, merchants should also beef up their self-service content. A growing number of shoppers consult FAQs and customer-contributed Q and A resources before even trying to reach live help, and 39% of Millennials turn to self-service help first and foremost. As the holidays approach, merchants should review Q&A content for top products, update FAQs, and incorporate common responses into core product page information.

 

Looking for more holiday readiness tips? Read the first and second blog posts in the series here. To ramp up the shopping experience for 2018, check out Kibo’s omnichannel commerce solution and gain access to strategy expertise during the holidays and year round.

personalization features updates for the holidays

Three Tweaks To Personalization Features For The Holidays

With the holiday season fast approaching, many merchants are hoping investments in personalization technology will pay off. Those who take the time to fine-tune the personalization features in the final weeks before the peak season can deliver the spot-on relevance that leads to sales growth.

Personalization has increasingly become a top priority for merchants, and with good reason. Consumers now expect shopping experiences to be tailored to their interests: forecasters predict fully $800 billion is set to shift toward merchants who deploy personalization technology effectively.

The key word, of course, is “effectively.” Far from adopting a “set it and forget it” mentality, merchants must devote ongoing resources to ensure that their real-time personalization features are culling the right data and serving the right products to the right individuals via the optimal touchpoints.

During the holidays, when shoppers are inundated with messaging and competition is fierce, relevance is more important than ever. During Kibo’s second client-only holiday readiness webinar, customer success specialists discussed winning tactics for ensuring that marketing and merchandising work in concert to maximize relevance. Among them:

 

Differentiate cross-sells and upsells. Recommendations on the product detail page are one of the longest-standing and most effective uses of personalization. Cross-sells, which display adjacent items such as accessories for apparel or electronics, and upsells, which encourage shoppers to consider higher-priced items in the same category as the product they’re currently considering, can both be effective means to encourage a higher total order size. 

For the holidays, merchants should consider which type of recommendation works best for which categories — or consider using both, as Kibo merchant Peruvian Connection does using a tab presentation. “Complete the Look” displays other items in the pictured ensemble, while “you may also like” presents upsells in the same category as the featured product.

 

The shopping cart is another potential opportunity to use individualized product recommendations — but in this case, merchants should fine-tune to ensure that whatever products are displayed won’t distract from order completion. Big-ticket or high-consideration items or products that require customization or special delivery requirements aren’t appropriate; instead, merchants should limit the potential products to items that can be added directly to the cart without deep study of the product detail page. Gift cards are a good choice to add to the lineup during the holidays, making it easy for shoppers to take care of another to-do on their lists.

 

Give triggered emails a holiday polish. Triggered emails deliver ultra-relevant messaging based on specific events, from joining the email list as a new subscriber to abandoning the shopping cart to completing a first purchase. The content and cadence of these emails should both be evaluated pre-holidays to ensure they deliver the expected messaging on time.

For example, new subscriber emails should emphasize holiday-centric features, from eCommerce site gift guides to gift finder apps and seasonal hashtag campaigns on social media. Transactional triggered emails to purchasers should include return and shipping information specific to the holidays, as well as promotion of in-store holiday events and services.

Timing is a factor, too: While many merchants send a first abandoned-cart email within 24 hours, that cadence may be too slow for sale days such as Black Friday, when discounts abound and top sellers are going fast. Merchants should also coordinate campaigns to ensure that triggered email series aren’t so frequent that they overload subscribers who also receive calendared promotions.

 

Test retargeting ads to ensure they’re cool, not creepy. Display, search, or social ads that target individuals who’ve abandoned the eCommerce site or their online shopping carts are an effective means of recapturing sales, with click-through rates ten times higher than generic display ads.  During the holidays, retargeted ads are more crucial than ever, reminding shoppers to return and finish their gift selections.

But as always, merchants must be aware of the line between creepy and cool, and ensure their ads don’t alienate shoppers. They should cap the frequency and duration of ads, and adjust settings for Black Friday and other peak sale days, when limited-time pricing may be in effect. They should also customize ad content for the holidays, including links to gift guides, gift card promotions, and other seasonal picks in the mix.

Finally, merchants should test retargeting campaigns extensively and make sure they work as expected, displaying only the most relevant offers, disappearing once a purchase has been made, and offering customer service support as well as product arrays and discounts.

Looking for more holiday readiness tips? Read the first blog post in the series here. If you don’t yet use real-time individualization to optimize the shopping experience, check out Kibo’s industry-leading offering and gain access to strategy expertise during the holidays and year round. 

 

Business man sitting at his desk

Expert Series: Personalization And Data Science

Welcome to the Expert Series. This is the second of three blogs highlighting experts at Kibo to give a deeper look into their areas of expertise. Today: Haowen Chan, Director of Data Science and Data Architecture, Kibo.

 

We hear about data science, predictive algorithms, and big data all the time. What is the difference between these terms?

Data science and big data are new labels that have popped up in recent years to describe modern activities in the industry that are a part of long-established fields in computer science.

As early as the 1960s we have the concept of data mining, which means extracting patterns and predictions from data. Likewise, machine learning, or the discipline of designing systems that adapt themselves to data in order to achieve a specific task, has been known for a similarly long time. The term computer science itself has been in existence since the 1950s.

In academia, these fields are highly diverse and draw from a wide variety of disciplines related to computer science, including statistics, information theory, optimization, and numerical methods.

Things changed as we entered the Internet age. As a population, we started doing more and more of our activities in electronic media where data is much more easily collected and aggregated. Activities that were traditionally performed in physical space, such as shopping, interacting with friends and collaborating with colleagues, accessing information, and engaging with entertainment all started becoming online activities.

Because of this, people started generating an increasingly significant volume of electronic traces of their activity. Information such as what you looked at online, who you know, and what you bought were now valuable sources that could be used in new applications that could predict helpful things such as what films you should watch, who you should know, and what content on the web is most relevant to you.

As a response to the need to develop ever more sophisticated algorithms over increasingly large volumes of data, technologists in industry and academia designed systems to support general-purpose computations over massive volumes of data in an efficient, scalable, and reliable way. Highly scalable parallel computation systems like Hadoop and Spark enabled enterprises to implement complex machine learning or data mining algorithms over very large data corpuses that previously were feasible only for institutions with dedicated supercomputing capability.

This heralded the age of big data – referring to the ability to not just collect, store, and retrieve vast amounts of information, but to perform the complex computations necessary for supporting modern applications of machine learning, data mining, and statistical modeling.   

The broad adoption of new, highly parallel software tools opened a new field of scalable systems engineering in addition to the known fields of statistical machine learning and data mining. In addition to the already-diverse skill sets in machine learning, a practitioner needed to be able to demonstrate the skills necessary for extracting and organizing the data for storage in a software system, and also the technical skillset needed to analyze, visualize, and implement applications based on the large volumes of data available. To distinguish this unique set of skills and differentiate from the more specialized machine learning researcher and the more general software engineer, people started self-identifying as data scientists. Today the term data science tends to refer to industry practitioners performing data analysis or software development of data-driven applications that involves building complex mathematical models using modern tools capable of handling the large volume and complexity of the data corpuses involved.

 

How are data science and big data changing the retail space?

I believe the fundamental definitions of engagement in retail are changing as a result of the increased instrumentation and intelligence of modern systems. We currently still have a lot of ground to cover in terms of simple facilitation: making it easy to find and order a specific product, increasing conversion rates, and so on. As catalogs and user bases grow, this problem will continue to be technically challenging. Technologies like search (using natural language, images, or inferring stylistic similarities) and personalized site experiences will get increasingly sophisticated.

However, the most advanced retailers are looking past that, toward concepts such as intelligent anticipation and inference of needs as well as direct engagement through social media and other online touchpoints. There are many potential applications of artificial intelligence (AI) in this space, for example intelligent sales or personal agents, and marketing agents designed to generate content specifically tailored to various online subcultures so that a brand can be more than one thing to more than one person. Retail has always chafed under the fact that advertising and sales interactions are crude and heavy handed. The holy grail here is for brands to use technology to directly integrate into the lives of individuals in a positive way, so that the retailer or brand is almost like a partner in your life helping you achieve your goals.

 

What importance should retailers place on data science? Where is it best applied?

There are numerous applications for data in retail. Many of these fall under Kibo’s product use cases. For example: multi-channel personalization and product recommendations; catalog management; order management and optimization, in-store and online data integration. In general, the challenges facing retailers in the data world fall into several categories depending on the maturity and capabilities of the retailer.

Data collection and instrumentation. This typically manifests in smaller retailers who have basic eCommerce systems (essentially, involving catalogs and orders), but no additional sources of data with which to leverage the more complex use cases like personalization. For example, customer registries may only track previous orders, and not site clickstream behavior. Perhaps marketing email open rates are not being monitored, and specific item engagement for these are not recorded. Alternatively, perhaps the metadata in the product catalog is incomplete. In each of these cases, the goal is to add additional instrumentation to start gathering valuable data. Kibo, for example, has observer tags that can collect the clickstream of users on our eCommerce sites.

Data integration. This challenge faces retailers who have multiple systems generating data but which are not integrated with each other. The problem then manifests as data silos. For example, a customer registry may contain all customer orders, but a clickstream tracker may track click data, but the data is stored in two separate systems, and each of them power an independent application which does not have a view of the other system. This limits the potential data applications deployable by the retailer and leads to increased cost of ownership of the data as each separate substore of data needs to be independently maintained. Investment in this is typically unfavorable to the retailer as many retailers do not wish to develop data processing and maintenance as a core competency.

The solution is to have a single core system that collects all appropriate data from various subsystems and applies the appropriate cross references to identify data linkages from separate stores. The new data store is then available for general purpose data applications, as we will discuss below. The Kibo data store is an example of such a data hub; using the latest data integration techniques, we pull information from various sources onto our big data framework to feed our data-driven applications.

Data applications. Once a retailer has achieved a high level of data integration, the challenge then becomes deriving maximum value from the data. The challenges here are twofold: (1) does your application suite support flexibly adapting its models and behavior to take advantage of various types of incoming data and (2) if you have an in-house data team with highly specialized domain knowledge of the specifics of your market, does your application framework support customization and interfaces that allow your data team to inject the results of their modeling and analysis without significant integration costs?

Kibo’s solutions offer a high level of functionality in both of these areas, and we are continuously working to extend them.

 

Q: How are you applying big data and data science to Kibo’s solutions?

Kibo’s big data architecture has been in production since 2011, making it one of the most mature deployments in the space. Of course, we have been constantly improving and rewriting the code, making it more streamlined, simple to integrate with, and efficient. Our systems use the latest technologies from open source, including Hadoop, Spark, and Mahout, and we combine these with proprietary high-performance libraries to perform fast inference at our RTX servers.

This extensible and well-maintained framework allows our data scientists to rapidly develop a wide variety of statistical models. Techniques used in our model include collaborative filtering, dimensionality reduction, many forms of regression, latent variable graphical models, reinforcement learning, and a wide variety of Bayesian inference methods.

These models drive much of the logic of personalization behind Kibo’s RTI product. As we continue to extend the architecture, we will bring the power of data science to all of Kibo’s product suite, improving the intelligence behind order management and fulfilment, as well as all tasks associated with the management of eCommerce. We’re working on creating a next-generation intelligent, optimized and adaptive e-commerce suite. Stay tuned for more as we unveil our new capabilities in the coming year!

 

For more information on personalization and real-time individualization, download The Ultimate Guide to Personalization.

The Case Of The Missing Omnichannel Strategy

Omnichannel is the goal, but where do retailers stand?

As the industry starts to focus more on how omnichannel technology influences the customer experience, it’s become important to see how far retailers have come, and where they currently stand.

View this infographic to explore what you might be missing from your current omnichannel strategy in regards to:

  • Fulfillment and Inventory
  • Personalization
  • Price Consistancy
  • In-Store Signage

 

The State of Omnichannel Commerce, Omnichannel Strategy

Omnichannel Experience

New Research: Kibo Study Reveals Crucial Gaps In Omnichannel Experience

There’s good news and bad news in Kibo’s latest survey of retailers’ omnichannel offerings. While merchants are making great strides toward a unified brand experience, the “last mile” of the omnichannel path-to-purchase leaves much to be desired for consumers — which means dollars left on the table for sellers.

Kibo’s 2017 “State of Omnichannel Commerce” report details the result of mystery shopping forays both online and offline at more than 30 mid-market and leading retailers.  On the surface, it reveals that the vast majority of merchants have adopted best practices when it comes to integrated online and offline experiences. For example, some 87% of merchants offer online shoppers the ability to view product availability at local stores, and store associates at 97% of retailers can view inventory enterprise-wide.

But a deeper dive into the results show that these implementations still lack the complete fluidity today’s consumers demand. To fully realize the potential of their universal inventory and fulfillment operations, merchants must:

 

Cater to mobile shoppers, not just BOPIS buyers. Kibo’s survey found that not only do most merchants display in-store product availability on their eCommerce sites, but in most cases, shoppers who locate relevant products can secure them immediately for pickup, with 77% of sites offering “buy online, pickup in-store” (BOPIS) or ship-to-store capabilities. Some 78% of consumers report having bought online to pick up in-store in the past six months, according to Kibo’s Consumer Trends Report.

But not every online shopper is set to whip out a credit card. Indeed, while two-thirds of online interactions with brands now occur on mobile devices, mobile transactions remain a small piece of the overall retail pie, which means that the vast majority of online sessions do not result in an immediate purchase. Mobile browsing and research does lead to in-store buying, however, influencing a whopping 31% of all retail sales — which means that sellers should cater to those shoppers heading out the door to buy in-store immediately.

Shipping and Delivery

Shipping and Delivery

So far, though, most merchants are missing a crucial component of the puzzle: just 35% of sites display inventory quantities, leaving consumers in the dark if they’re unwilling to wait up to a day for their order to be processed, picked, and packed via BOPIS, or even longer for ship-to-store orders. Shoppers headed to the store now want to know how many items are in-stock and even where to find them; especially during the holiday season when top sellers fly off the shelves, such information is crucial. Mass merchant Target’s product page display of fulfillment options both offers the ability to purchase on the spot and lets shoppers know exactly how many items are in store, as well as the aisle number.

 

Untether store associates from the register when conducting omnichannel business.  Enterprise-wide inventory visibility for store staff is a boon, given that more than half of consumers expect associates to be able to find items that are in stock elsewhere, whether on-site or at another location. But while access to inventory for associates is nearly universal, they can only rarely access the information they need while in-aisle with customers. Rather, fully two-thirds of associates must return to a register or terminal to look up inventory. As anyone knows who’s had to trek from a big-box store aisle to the nearest service desk, such an undertaking is rarely quick — undercutting the flow of the associates’ sales interaction and reducing convenience to the shopper.

Furthermore, if associates go on to help shoppers purchase one of those items located elsewhere, a whopping 92% of them must conduct those transactions at a register or computer terminal, rather than completing orders in-aisle. And 24% of associates can’t even help place such orders at all, forcing shoppers to fend for themselves if they want to claim items at other locations. In an era when self-checkout is ubiquitous and checkout-free prototypes such as Amazon Go are establishing new expectations for seamless in-store transactions, such hurdles are increasingly unacceptable. Merchants should invest in solutions such as Kibo’s mobile point of sale to give associates the flexibility they require to meet shoppers’ needs wherever in the store interactions take place.

 

Read more about Kibo’s mystery shopping survey and download the full report for more omnichannel insights, including:

  • which personalization techniques are most common — and most overlooked;
  • popular price matching strategies; and
  • assessment of store layout and signage
stages of machine learning

Four Stages of Machine Learning For Retail

Machine learning is becoming a common phrase, but understanding what it means is an entirely different matter. In this blog post we will explain what machine learning is, how it works, typical examples, and how machine learning is used by retailers to increase the personalization experience for their customers.  

Machine learning is an application of artificial intelligence that makes predictions based on assumptions acquired by surveying large amounts of labeled data. Artificial intelligence refers to systems that appear to think. It encompasses a wide variety of technologies, including robotics, natural language processing, and speech recognition. Machine learning is one of the most well-known applications of artificial intelligence and it is common in complex or dynamic domains. It is the only one currently being applied for retail purposes. 

 

Stage One: Obtain and Clean Data 

Real Time IndividualizationThe first stage of a machine learning program is to obtain and clean data. This is the data that the machine will use to make assumptions about future data, so it is of utmost importance that the data is clear and correctly labeled. The large volume of data required makes this a time consuming and important step.  

The example on the right shows a sample of cleaned data. Note that all numbers are facing the same direction, are the same size, and correctly labeled.  

 

Stage Two: Storage and Big Data  

As the clean data is acquired, it must then be stored. Machine learning relies on big data and thus storing the data is another large step in the process. There is a significant infrastructure investment required in order to make big data storage and management possible. Choosing a storage system involves choosing a product that can perform general computations over very large volumes of data in a highly scalable and parallel way.  

 

Stage Three: Model Learning 

The key step in the machine learning process is model learning, also sometimes known as training. This step is concerned with the creation of the machine’s knowledge about the relationships between inputs and outputs in its environment. In this step, the program begins analyzing the data, trying to find patterns and insights that can be turned into actionable predictions. The program steps through the data multiple times, each time becoming more finely tuned. The more times the system surveys the data, the more accurate the model becomes. More importantly, the larger the base of data fed into the model, the stronger the assumptions can be made. 

 

Stage Four: Model Creation 

After training, the model is created. A model refers to the collection of assumptions about how inputs relate to outputs. It is the artificial intelligence that will determine the response to queries. The only way to know if the model is accurate is to test it.  

 

Machine Learning Application 

Machine learning has become an invaluable tool to retailers to predict customer behavior and tailor recommendations. It can be leveraged at several levels of customer specificity. To learn more, click here 

Examples of machine learning can be found in most every home. Siri and Alexa are two of the most prominent examples. Classified as virtual agents, they employ machine learning for the purpose of understanding human speech and intention. They use data from the web and the requests of their users to understand different accents and ways of making the same request. Through data modeling they have learned to interpret speech and determine the action or information that the user is requesting.  

Personalization engines use the same machine learning techniques to power different outcomes. The data they analyze is not human speech but the actions of customers. Every single action that a customer takes – or doesn’t take – is fed into the model for the recommendation engine. The events that are commonly recorded include search terms, time spent on a specific page, which recommendations have been shown to that user, were clicked on or not, were added to cart, and what was eventually purchased. This data is not limited to digital interactions either. With a fully integrated customer experience, the model will include purchases made at brick-and-mortar stores as well.  

Buy It Again is an exemplary feature of machine learning. The machine can analyze how often a specific purchase is made by most people (for example razors or paper towels) and offer recommendations only at the time intervals prescribed by the data analyzed. This also keys into gift shopping versus personal shopping. It is one of the greatest difficulties of customers to buy a gift for someone else and be plagued for weeks with ads for that particular item. It can also be leveraged to stop showing an item once one has been bought. Once you buy a lawnmower, you really don’t need another for quite some time. Machine learning solves for these common failures.  

Models trained from finite data always include a certain measure of uncertainty. To test this uncertainty, the machine allows for A/B testing as a way to raise its level of certainty. During A/B testing, hypotheses pertaining to similar contexts or segments can be tested against each other to prove which is more effective. 

 

In conclusion, though machine learning is often thrown around as a high-level concept and mysterious technology, the steps it goes through are understandable by any layperson and an understanding of the process can lead merchandizers to make better marketing decisions.  

MAP vs MSRP

MAP VS MSRP: What Pricing Strategy Works Best for eCommerce Success?

Merchants know that price is an obsession with customers. Meeting expectations for discounts while juggling in-store versus online promotions and avoiding channel conflict is a challenge that sellers have tackled using pricing tools such as the MSRP and MAP. But increasingly, just one of those methods retains relevance in the world of omnichannel retail.

By multiple measures, price continues to reign supreme with consumers. Kibo’s 2017 Consumer Trends report found that price is the top reason 70% of shoppers choose to purchase from a particular Web site — dwarfing the next highest factor, brand name, by a factor of four. And  60% of shoppers say attractive prices prompt them to shop with favorite retailers, according to consulting firm PwC — close to double the percentage who chose brand trust and item availability, which were the next most important factors.

The quest for discounts has led online mass merchants to strategically offer deep discounts, making up in volume what they erode in margin. In this race to the bottom, small- to mid-sized merchants have struggled to keep up, using dynamic pricing tools to track competitors’ product price fluctuations and offering price-drop alerts and other discount-focused features for shoppers.

The situation is further complicated by increasingly sophisticated personalization tools, which enable merchants to send individualized offers to consumers based on past purchasing behavior, and apps such as RetailMeNot that act as discount clearinghouses, enabling shoppers to harvest promo reward codes without building relationships with individual brands.

And then there are the unique challenges faced by brand manufacturers and their retail partners. Fully 48% of brand manufacturers now sell products directly to consumers online, creating potential for channel conflict when it comes to price. And retailers selling manufacturers’ goods find themselves competing not only with mass merchants, but with cut-rate sellers whose products are featured on third-party marketplaces on sites such as Amazon, Walmart, and eBay. Those with physical store outlets must additionally have strategies in place for shoppers using their phones to comparison shop and requesting price-match discounts.

Given all of these competing demands, the promise of establishing pricing consistency is alluring. Merchants have long turned to tools such as the Manufacturer’s Suggested Retail Price (MSRP) and the Minimum Advertised Price (MAP) to attempt to establish pricing parity and level the playing field against deep discounters. But do these strategies, which have been around for decades, still apply in the age of omnichannel retail?

 

The MSRP: Almost a Dinosaur?

In theory, the MSRP is the price that represents what manufacturers believe products are worth in the marketplace, and the price they believe retailers should set. But as anyone who’s haggled for a car knows, the MSRP more typically is just a starting point from which retailers deduct discounts to spur purchases.

In fact, the tendency never or rarely to charge the displayed MSRP has gotten eCommerce merchants into trouble, with complaints being filed in the courts on behalf of consumers who claim the list price is nothing more than false advertising designed to make discounts seem bigger.

Legal challenges aside, the MSRP is less relevant in an age of personalized offers and dynamic pricing, when product prices vary depending on availability, seasonality, and the shoppers’ own purchasing history, location, and situation. Perhaps for those reasons, online behemoth Amazon.com has been experimenting with eliminating list price displays.

Setting an MSRP can still be a useful exercise for manufacturers; by calculating the cost of production and assessing the market forces that might drive demand, manufacturers can use the MSRP to derive wholesale pricing and sales programs.

 

MAP: Work it to make it work

Using MAP agreements, manufacturers can standardize among its resellers the lowest price at which products can be advertised. In practice, resellers still retain pricing flexibility: online, they can typically apply discounts in checkout, or send personalized promotional offers, without violating MAP agreements, which apply only to broadly-advertised pricing available to everyone in paid search ads or shopping search engines.

MAP pricing strategies can be helpful both to manufacturers and retailers. By standardizing advertised price, MAP pricing strategies enable retailers to put the promotional spotlight on differentiators such as service, product support, customer communities, in-depth content such as buying guides, and flexible fulfillment policies — all of which can help small- to mid-sized retailers compete against bargain-basement mass merchants.

For manufacturers, using MAP policies as a baseline enables collaboration with resellers that can help brands meet consumers’ expectations for service and product availability. For example, manufacturers can route direct Web site orders to retailer for fulfillment and share proceeds of the sale — potentially speeding delivery and giving shoppers the additional option of in-store pickup. Kibo merchant Mizuno USA uses just such a fulfillment program, sending online orders to nearby retailers for swift fulfillment.

But MAP policies require an investment in resources to execute successfully, as manufacturers must both communicate the policies to retailers and follow up with monitoring and enforcement. At a minimum, manufacturers who set MAP prices should:

  • Reconsider marketplaces. Undercutting prices is rampant on third-party marketplaces,  where merchants offering the lowest price often win the coveted “buy box” prompting shoppers to add items to the cart. Manufacturers may decide to limit participation on marketplaces for authorized retailers and resellers as a way to keep their products out of the fray.

 

  • Monitor paid search ads, and own branded terms. Locking down brand names and product titles — using trademark enforcement if necessary — ensures that bargain-basement resellers won’t steal the limelight from manufacturers and their partners in Google Shopping ads and other high-visibility placements.

 

  • Build in flexibility to accommodate retailers’ needs. MAP violations jump by 15-20% during peak holiday sales as retailers vie to offer gift buyers the bargains they seek — so manufacturers should cut resellers some slack during such highly competitive periods. In addition, manufacturers should consider whether to partner with authorized resellers to offer sought-after free shipping promotions or free gift cards with purchase as a way to sweeten sales without sacrificing pricing.

 

What pricing strategies are you employing to establish consistency across touchpoints?

seamless customer experiences

Creating Seamless Experiences for Customers: The Overlooked Facet of Individualization

Commerce professionals have noticed that “omnichannel commerce” was simply a building block for the next customer driven desire: unified commerce for better customer experiences. The industry has been talking about this all year, and commerce professionals are looking for ways to deliver on better customer experiences.

Many things contribute to this concept of unified commerce for better customer experiences, and today we will discuss a frequently overlooked facet: Individualization. For the purposes of this article, “individualization” is not “personalization” as personalization simply cannot provide the technology needed for a holistic view of the customer. More on that here. 

History has shaped us, now press on to the future

First generation personalization was a great start on the personalization path, but the current demand is for something greater. One of the key limitations of first generation personalization systems is that they were built for a single channel like website or email. Depending on which channel the system was designed for, they have a bias for how they collect and store data. For example, website oriented systems will collect and store data in cookies in the customer’s browser, and email systems will build a database that is keyed off of the customer’s email address. The two aren’t exactly connected.

This was a great starting point, but now it has become imperative that we are able to provide personalization across all devices and touchpoints. The evolution of the personalization solution can be compared with the natural evolution of the customer, from one touchpoint to multiple touchpoints; from silos to omnichannel to unified commerce.
You can’t have an individual experience if you don’t know how to interact with the individual consumer across devices and touchpoints

Personalization (or as we like to call it: individualization) allows the brand or retailer to see consumer behavior across all different touchpoints. Compare this to a siloed experience: seeing only website or only in-store or only mobile or only call center behavior. If you can only see a fraction of what the consumer is doing, you are missing out on major opportunities to provide them with a better customer experience and your company with more sales.

Customers are feeling the division made by the silos, and are taking their phones into their own hands in an effort to connect channels: some 77% of U.S. shoppers have used their smartphone in store to help them shop. With consumers already using their mobile devices in store, it begs the question: Does your technology allow you to see that the consumer is in your store using their smartphone?  Individualization sheds light on what people are doing holistically.

What data do you have, and what do you do with it? 

There are many systems and programs to gather customer data across touchpoints, and what you do with it makes all the difference. Let’s break this down into two goals.

Goal number one: Capture as much information as possible to understand customer behavior across touchpoints in one repository.

The company sees many benefits from understanding customer behavior, but what does it do for the individual customer?  Enter goal number two.

Goal number two: Communicate with the customer across touchpoints. Don’t just take data, but now it’s time to push it back out, and actually provide individual experiences.

Data comes from everywhere, and the data that is currently the most limited is store data. Right now store data is mostly purchase data. However, we see it’s evolving rapidly (enter beacons) which will provide more store data once they are more widely used. The great news with machine learning individualization systems is the more data you can gather, the better. Great systems can easily scale to take on more data.

Consider this customer expectation from Kibo’s Consumer Trends Report — 2017 Edition: 74% of consumers expect store associates to access their customer history data when they visit a store after purchasing online. This expectation simply connects two touchpoints: online and in-store. Let’s take it further as you consider the following example:

Imagine an in-store experience where the customer gives their email address to a tablet-enabled store associate and together they look up purchase and browsing history. The associate recommends a few products based on that history. The customer informs the associate they are not interested in that product. The associate enters this preference into the tablet.
If they were simply taking a local note, the only other person who would know about this preference would be the next associate using the tablet. And imagine the frustration of the customer if they keep seeing that same product recommended to them over and over again while shopping on desktop and mobile, despite having already made their preference clear.

True individualization will give the store associate the opportunity to input the consumer preference into the software, and that data will then be pushed out across all touchpoints. Suddenly, the consumer feels like they are known to the company, and that their preferences are actually taken into consideration while shopping with that company.

Not every company has a store associate for every customer who walks in the store, but that’s okay because customers can also receive this kind of individualization via a mobile app. Strongly consider how a mobile app will benefit your customers, and if you already have one, determine if it functions as you need it to.

Seamless customer experiences

Individualization leads directly to better in-store experiences. It’s not hard to imagine how it will also lead to a better call center experience, mobile experience, or desktop experience. These are the seamless experiences customers are looking for.

A chain is only as strong as it’s weakest link, and this is a great way to look at unified customer experiences. Does your omnichannel strategy have a weak point? The technology industry uses the word “smart” to imply connected devices or products, so take inventory and enable all  touchpoints to be smart touchpoints. Companies who want great customer experiences have smart channels. These channels capture data and then push it back out.

Remember back to that in-store example. The system the associate was using is a learning system, which collects all the information available and puts it back into the system. In our example the customer wasn’t interested in the product, but the opposite could very well be true. The customer may have been shown a recommended product, the associate had the benefits and features right there on the tablet, and the customer decided to make the purchase. The associate can then add all of that information and that experience into the system to better inform future communication with this customer. They are able to close the feedback loop, and the learning system becomes smarter.
It today’s market, the search for seamless customer experiences must include individualization. Smart channels bring connection between companies and consumers, which in turn leads to great customer experiences.

mobile paid search

Five Ways To Boost eCommerce Success In The Era Of Mobile-First Paid Search

Mobile paid search was once considered a bargain buy. Now that the majority of searches occur on mobile devices, however, CPC bids are catching up to reality. ECommerce merchants must invest their paid search dollars more wisely than ever to execute an effective mobile paid search strategy that drives omnichannel sales.

Smartphones are now the dominant search tool in terms of both clicks and search volume, according to a study from Google and ROI Revolution, with phones surpassing desktop and laptop computers and tablets for the first time in 2016. And 55% of clicks on ads in the popular Google Shopping format are on smartphones, according to Marin Software.

This surge in mobile paid search usage is boosting CPC rates for search ads targeted to mobile devices. Although smartphone ad rates still trail desktop by some 20%, according to Marin Software, that gap has closed by 9 percentage points, or 30%, in the past year. During the peak holiday shopping period, competition is particularly intense, with mobile CPC rates jumping 5% year-over-year in 2016, compared with price declines for desktop computers and tablets, according to the Google/ROI Revolution study.

Given these pricing pressures, small-to-mid-sized merchants must wield their paid search investments more carefully than ever to ensure maximum impact. By matching segments with the appropriate ad types and content, sellers can demonstrate their brand’s relevance and boost omnichannel conversion. Among the best practices:

 

Check landing page speed. One key to driving mobile search ad conversions has nothing to do with the ads themselves. Mobile site speed is crucial to winning conversions, with pages that load in less than 3 seconds earning peak conversion potential, according to the Google/ROI Revolution study  — so merchants should do everything in their power to ensure that paid search ad landing pages not only clearly convey key product information, but are also swift to load on smartphones.

Don’t forget the “phone” part of smartphones. Merchants should take advantage of click-to-call features within paid search ads, and ensure that the numbers listed actually reach live people. Geo-targeting ad content to feature local store phone numbers puts shoppers in touch with merchants’ local representatives, who can help them address questions with store resources in mind.

Geo-target store-related promotions. With mobile commerce conversion rates still lagging mobile browsing and research, merchants would do well to remind searchers of the services and promotions available at nearby outlets. In addition to experimenting with Google’s Local Inventory feature, merchants can promote buy online, pick up in-store (BOPIS) and other fulfillment services, along with store events.

In addition to geo-targeting, store promotions can also be prioritized for delivery to prior mobile site visitors who left without purchasing — in effect, creating retargeting campaigns letting shoppers know they have alternatives to buying on their phones.

Narrow the field for Google Shopping ads. With competition for popular Shopping ads intensifying, and screen real estate limited on mobile devices, merchants should do their utmost to use these compelling image-and-text ad formats as efficiently as possible. To do so, they should experiment with:

  • Targeting prior buyers. Those already familiar with the brand, who have presumably enjoyed a positive purchase experience, are more likely to act on a Shopping ad alerting them to product availability from a trusted merchant.
  • Showcasing exclusive products. Attempting to compete in the Shopping space for commoditized products is a losing proposition for most merchants, whose paid search budgets are smaller than mass merchants and whose product prices may not compete with big players’ deep discounts. Instead, merchants should shine the Shopping ad spotlight on their unique finds and private-label items. To promote visibility of these items for shoppers not yet familiar with the brand’s niche offerings, merchants should use natural language descriptors in the ad text.
  • Bidding on specific vs. broad search terms. To reach shoppers who are nearing a purchase decision, merchants can throttle their bids to focus on keywords with a higher degree of specificity, such as precise brand or even product names.

Experiment with paid search spend on social platforms. Social networks are overwhelmingly mobile, with close to 80% of time spent on social media occurring on mobile devices, whether through apps or the mobile Web, according to measurement firm comScore. For that reason, merchants should think beyond Google when it comes to making placements, and explore paid search, retargeting, and programmatic ads on platforms such as Facebook and Youtube, where fully 70% of viewership occurs on mobile devices.
How are you optimizing paid search dollars for mobile audiences?