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. 

 

artificial intelligence ecommerce

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.

re-platforming

4 Insights to Remember When Facing eCommerce Replatforming

Replatforming horror stories abound. It takes too long, it’s too much money, sites crash, sales plummet; it’s an eCommerce bloodbath. While these failures may make a homegrown solution look tempting, before you give up on all ready-made platforms because of bad experiences, look to your vetting process instead. A solid, thorough vetting process will help you make an informed and successful replatforming decision.

Whether you are considering replatforming with your current vendor, or selecting a more modern eCommerce provider, here are some ideas to help you make the best replatforming decision for your business. Get to the next level of eCommerce with these four key insights:

Gain a Comprehensive Understanding of Your Business:
Understand your needs and requirements for a new platform, and gain a complete understanding by looking at the whole business. Don’t leave a stone unturned. Successful eCommerce programs traditionally involve multiple departments. Be sure to reach out to all stakeholders in your company to ensure you have captured every necessary requirement. Finally, don’t only make a list of your necessities required today. Also create a list of features you might need in the future.

Now that you are armed with the knowledge and understanding of what your company needs to succeed, begin looking for and vetting companies that will meet and surpass those needs. Create a personalized request for proposal (RFP) that allows you to make an apples-to-apples comparison between your short-listed vendors. And don’t forget to research vendors who have a forward-focused roadmap that will take you into the future of commerce in a stable, powerful, and lucrative partnership.

Understand the True Replatforming Total Cost of Ownership:
The next deep understanding that is imperative to your replatforming decision is the true total cost of ownership of this project. Cost can’t just be looked at from an immediate output point of view; cost over time should also be considered.

The true total cost of ownership can be a little difficult to obtain at first, but once you do, it will illuminate your path forward. Some costs to keep in mind are:

  • Platform/subscription pricing
  • Implementation cost
  • Hosting and managed services cost
  • Commerce strategy cost
  • Upgrade path and cost
  • Support and maintenance
  • Initial and ongoing training

These are only a selection of costs to take into consideration. Good eCommerce vendors should be upfront and open about services expenses, including support, upgrades and training, when working with you to form a partnership. When evaluating whether to replatform with your current vendor or move to a new one, look at the entire picture before making a decision.

Research Historical Security and Platform Stability Issues:
There have been an unprecedented amount of security breaches in the recent months. These security breaches not only cost a lot of money and time to recover from, but they cost a loss of customer trust. As you vet a new platform, look to it’s platform security and compliance. What platform vulnerabilities have been exploited in the past? Are they level 1 PCI compliant? Level 1, with more rigorous testing and third party verifications, offers the highest level of account security throughout the credit card transaction process.

Security is an important part of the vetting process, but so is the speed, performance, and scalability of the platform. A certain platform may meet your needs right now, but by the time the platform is implemented (depending on implementation time) you may have outgrown it entirely. Essentially: make certain the platform will surpass expectations no matter how your business performs day to day or grows over time. Along those lines, evaluate the vendor’s hosting solution and if they have plans to change hosting anytime soon. Whatever their hosting solution, evaluate it against your uptime objectives, if it will scale based on your needs, and if they have a disaster recovery plan.

Consider the Depth of the Partner Ecosystem:
A great platform also has a great third-party community associated with it. But not all partner ecosystems are created equal. Great eCommerce vendors care about their partner relationships and how they relate to you and your business. Great eCommerce vendors have vetted their software vendors. Great eCommerce vendors take responsibility for the quality of their partner integrations.

The responsibility then lies to you to vet the vetting. What qualifications did the eCommerce vendor use? Additionally, what kind of access will you have to third-party vendors, and how does the platform manage them? If a platform’s third-party community is certified and endorsed, you can confidently move forward knowing that only high quality vendors work with your platform, which in turn bring value to the solution.

As you think about the future of your business, it becomes apparent that the platform you choose must help your business thrive no matter what the future may hold. Your omnichannel future is only as bright as the capabilities and forward-focus of your order management and eCommerce platform(s). In today’s competitive retail space, your software can’t simply conquer your current problems, it has to conquer what you will need in two to five years.

In summary, replatforming is a huge undertaking, with many different points to consider. A successful replatforming can depend heavily on the quality of your vetting process. These tips have hopefully helped reframe how you think about replatforming and set you up for success. For more information and additional tips, take a look at The Ultimate Guide to eCommerce.

Online Purchases optimize your ecommerce

Code Freeze Already? 4 Ways To Optimize eCommerce Sites For Holiday Success

As September rushes along, the holiday season is fast approaching — and with it, the period known as “code freeze,” when eCommerce merchants avoid launching new features or making fundamental functionality changes that might malfunction and negatively impact the bottom line. But merchants still have the opportunity to make small but mighty changes to optimize sites for peak sales.

The holiday season continues to be crucial for merchants: in 2016, fourth-quarter online sales accounted for close to 32% of eCommerce revenue for the year overall. And with the Web influencing over half of all retail sales both in-stores and online, it’s crucial for retailers and online-only sellers alike to maximize opportunities for engagement and purchasing via their eCommerce sites.

To minimize the risk of glitches during this crucial season, the technological lockdown for the Web site usually begins with the fourth quarter or shortly thereafter — which means that by now, the window of opportunity has shut for merchants to debut major new initiatives. But that doesn’t mean all hope for improvement is lost.

In the first of three clients-only holiday readiness webinars, members of Kibo’s strategy and customer success teams revealed a checklist of tweaks merchants can still undertake to ensure they make the most of holiday site visitors. Among them:

Optimize on-site search for mobile. Given that some 30% of shoppers use on-site search and they’re twice as likely to convert when they do, merchants would do well to vet functionality carefully — and that goes double for mobile. Given that mobile users are more likely to cut to the chase with a keyword search rather than squint their way through drop-down navigation to browse on small screens, sellers should analyze mobile on-site search logs and fine-tune accordingly. Replacing industry jargon with search terms consumers actually use, ensuring terms such as “sale”, “store hours”, and “shipping” shortcut to the relevant categories and customer service information, using predictive search suggestions to highlight relevant items and categories, and creating a rich “zero results” page that doesn’t bounce shoppers off the site are among the fixes merchants can enact now to drive relevance during the peak season.

Keep an eye on picture sizes. It’s an oft-cited statistic: more than half of consumers will abandon Web sites that don’t load within three seconds. But in reality, the expectations are higher still: Google found that when page load time increases from 1 to 3 seconds, the probability that consumers will bounce off the site increases 32%.

While code freeze means that major server overhauls and deployment of content delivery networks are no longer feasible before the holiday rush, merchants can still improve page load times by focusing on individual content assets — starting with photos. Given the outsized importance of visual strategy for brands and the wealth of still and video images generated by sellers and social followers alike, eCommerce sites are increasingly crammed with photos that can make or break site speed.

Accordingly, merchants should vet batch processing routines to ensure images are being sized appropriately, and consider manually reviewing image assets connected with their top 50 products. In addition, they should scrutinize and possibly scrap elements such as photo slideshows, which garner click-through rates of just 2 to 4 percent from the second slide onward, according to the Kibo webinar.

Test the gift card purchase process. The holidays are the year’s top season for gift card purchases: 61% of 2016 holiday shoppers said they hoped to receive gift cards, and as a result, some 33% of the $28 billion in gift cards purchased for others were bought during the Christmas season.

To take advantage of this opportunity, merchants should thoroughly vet their sites for gift-card prominence and ease of use — not only by using the desktop and mobile versions of the site themselves, but by assembling an ad hoc testing team of colleagues, parents, and teens who can step through the gift-card purchase process and uncover stumbling blocks on the path to purchase.

Lay the data groundwork for 2018 now. Hopefully, merchants have used 2016 holiday data and sales as a barometer for 2017 strategy — and they should plan to do the same this year so that next year’s planning is based on solid information, not conjecture.

Any gaps in analytics tracking or customer profiles that were uncovered when 2016 results were analyzed should be rectified now, and tests run to ensure that data is being collected accurately,  including from mobile Web sites and apps. In addition, merchants should optimize their analytics programs in advance by loading in milestones such as promotional campaign start and end dates, scheduled email sends, and key events such as Black Friday; that way, as results roll in, they can be analyzed in the context of the seasonal calendar.

Furthermore, naming conventions for source codes and promo codes should be finalized for consistency and catalogued for future reference, making future results analysis as clear as possible.

Check back for further highlights from Kibo’s holiday readiness series. Want full access? Consider becoming a Kibo client and benefiting from a world-class omnichannel commerce platform paired with expert strategy advice and custom insights during the holidays and year-round.  

Distributed Order Management

Expert Series: Distributed Order Management

Welcome to the Expert Series. This is the first of three blogs highlighting experts at Kibo to give a deeper look into their areas of expertise. Today: Mark Wright, Senior Solutions Engineer OMS.

 

 

What is distributed order management?

Distributed order management solves for the practices and technologies needed to fulfill merchandise orders in ways that meet today’s complex business and consumer requirements. It means how orders are fulfilled across all systems to optimize all processes. This includes things like drop ship, 3PL, routing & splitting, and workflows for every unique use case. It supports the omnichannel expectations of the consumer, from ship-to-store and in-store pick up, to ship-from-store, vendor drop-ship, return-to-store, and everything in between.

Is there a difference between traditional order management and distributed order management?

Traditional order management is fairly basic in scope, purposed to satisfy the requirements of a relatively static company. It tends to utilize heavy on-premise architecture, be cumbersome and often inaccurate, and limits flexibility, agility, channel growth and potential. In contrast, distributed order management is built to support the flexibility required by today’s dynamic growth demands, is most effectively cloud-based, is much more efficient and accurate, and is optimized to leverage all inventory everywhere it exists, fulfilling consumer demand using every means possible.

How can retailers or manufacturers gain a competitive advantage with distributed order management?

In today’s world, inventory data tends to be segmented and siloed, getting stuck and lost among various systems that do not properly communicate with one another in an efficient manner. The result is merchants not being able to have an accurate picture of inventory across the entire enterprise, consumers not empowered with data to inform their purchase decisions, and consumer touchpoints (digital and physical) lacking reliability in providing accurate answers.

The purpose of distributed order management is to logically assign and source orders across all systems and processes. The best way to achieve this is to bring together all inventory across all physical locations to provide a comprehensive and unified view of inventory available to promise. With all inventory known, merchants can expose inventory to consumers and empower them with the information to drive their preferred method of purchasing the product, call center agents can answer questions accurately and make smart decisions when needing to manipulate orders, and merchants can efficiently merchandise, plan, promote and restock to maximize sales, margins and positive consumer experience. These are the advantages achieved when using distributed order management.

What are some tips you can provide retailers when implementing order management?

Invest time in defining end-to-end integration points prior to implementation to avoid scope creep. Order management lives at the center of a merchant’s eCommerce ecosystem. As a critical component, it is important to thoroughly and meticulously map out all necessary integration points to connected systems prior to beginning implementation. Define all of the use cases in detail, determine which systems have a role and what data they need to perform the role, and map those appropriately. Systems’ connectedness is paramount to uphold end-to-end omnichannel commerce use cases.

Don’t neglect to engage store operations in planning and implementation. eCommerce and supply chain teams will have great ideas and core objectives for the OMS, but often may be designed and implemented in a way that inadvertently negatively affects retail operations. As one of the only physical consumer touchpoints of the program, it is critical that they are not only supported, but also sensitively consulted in the design phase in order to avoid unnecessarily overly complicating brick and mortar operations and to maximize positive consumer interactions in store across all commerce channels.

Is there an advantage of a single vendor for all of your omnichannel commerce needs?

Many organizations invest in technologies to support their omnichannel retail objectives over a period of time as they evolve and mature, and this often leads to a large number of technology vendors and solutions in the solution set. While this is not a flawed approach, it often requires an unnecessarily complex integration matrix that introduces gratuitous complexity and an unstable or unreliable stack due to the intricate dependencies among the various pieces of tech. This can be an especially painful challenge for Big Retail on heavy on-premise software with aged version integration challenges when they endeavor to keep pace with consumer expectations and modern trending technology.

Selecting a single vendor to support multiple facets of your omnichannel program not only reduces your integration complexities leading to greater reliability, but it also slashes the overhead required to maintain, monitor, and repair the connections among critical systems. Vendors offering an extensive suite of modular class-leading solutions that solve for multiple needs in your omnichannel playbook offers value beyond the solution(s) it brings. The total cost of ownership valuation combines revenue lift and technology cost with the variation in program management and maintenance overhead, rendering the procurement decision in many cases indisputable.

What is the value of multi-tenant SaaS specifically for OMS? Why not private cloud or on-premise for OMS?

Customer expectations are constantly evolving, and in retail today the message is becoming undeniably clear for traditional retailers: “evolve or perish.” Many brands and retailers risk focusing their modernization strategy too myopically on the digital channel alone. While the web presence is a fundamental component of remaining competitive in retail today, omnichannel distributed order management is the critical element required to support the end-to-end consumer expectation. Not all order management systems are poised to continually adapt and evolve with industry and consumer changes, however.

Multi-tenant SaaS order management solutions operate all customers on the most current (and only) version of the software in the cloud, which means that there is no dreadful upgrade path or process to endure. Kibo, as the premier multi-tenant SaaS OMS solution in the market, is focused on continuous innovation, released for client benefit at various cycles throughout the year to ensure clients remain equipped with the latest in cutting-edge distributed order management capabilities to maintain their competitive advantage and winning strategy in the marketplace. Other cloud-based offerings in the market often try to disguise themselves to mimic the summation of this value, but the reality is private cloud and individual-instance SaaS fall short in the upgrade-free, hosting-hassle free comparison. When it comes to the critical component of distributed order management, the unbeatable choice to remain current and competitive is the agile, efficient and effective multi-tenant SaaS solution.

 

 

If you wish to learn more about Kibo’s order management system, please click here

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.  

Amazon disrupts grocery

Has Amazon Disrupted Grocery For Good?

We’ve seen many traditional industries give way to disruptive technologies: Kodak, Blockbuster, and the cell phone to name a few. In each of these situations, an established way of doing things—taking film photos, renting movies from a store, using a non-Internet enabled phone—was usurped by a technological innovation.

Over the past few weeks, and the flurry of reporting surrounding Amazon’s acquisition of Whole Foods, the question has become: is this grocery’s moment?

In the 1930s, the supermarket itself was a disruptive innovation, using economies of scale to lower prices and provide goods on an unmatched scale. The supermarket eliminated the need for a separate bakery, deli, butcher, florist, and other services – bringing them into one location, under one roof – and putting many out of business. With eCommerce powerhouse Amazon taking over Whole Foods, it’s possible we may see the same consolidation happen, albeit digitally, potentially changing our relationship with the brick and mortar supermarket as we currently know it.

While there have been unsuccessful attempts to change the grocery business in the past, the past does not dictate our future. Here are a few reasons why this time may be different:

  1. Growing eCommerce Sales
    Global eCommerce sales are expected to grow to $1.915 Trillion by 2018. Consumer’s comfort level with eCommerce is increasing, due to the convenience of being able to shop at any time. This tailwind is pushing both grocers and consumers to explore new channels to shop consumable retail items. For example, Wegmans just announced a new partnership with Instacart to provide grocery deliveries in suburban areas. As customers transition their consumption of goods from brick and mortar to online, we will see competition and share of wallet allocated to digital grocery purchases accelerate.
  2. Brick & Mortar + eCommerce = Value
    Buying Whole Foods is a nod to the fact that selling groceries requires brick and mortar locations, either as a fulfillment center for neighborhood deliveries, or as a pickup location for consumers. This is profitable for retailers, too. As customers come to pick up their items, they make another purchase at least 40% of the time.  We’ve seen this transform other industries—apparel retail, as an example—as competition increased to provide customers an elevated customer experience that is differentiated from the competition.
  3. Mobile Is Ready
    An annual retailer survey by Forrester Research Inc. ranked mobile at the top of the list of strategic priorities for the fourth year in a row. And 94% of smartphone users look for local information on their device, and 90% take action after the search, according to Neosperience.  To create sticky retail experiences, retailers must optimize their mobile experiences to capture share of wallet from other channels. As grocers gear up to compete in a digital world, mobile will undoubtedly play a role in creating the grocery store of the future.
  4. Others Have Paved The Way
    Others have paved the way for “unconventional” grocery experiences. Consider meal kit services such as Blue Apron, Hello Fresh, or Plated, which have a great following:  Seven in 10 meal kit purchasers are still actively buying them. Also consider produce boxes from local farms, Peapod, even Amazon Fresh, that are offering alternatives to the typical supermarket fulfillment experience.  Because others have paved the way, consumers will be more likely to embrace alternative grocery on a larger scale.  Amazon’s purchase of Whole Foods might be just the shot in the arm the grocery industry needs to start moving toward a digital revolution.
  5. Consumers Are Ready 
    According to a Nielsen and Food Marketing Institute study, More than 70% of consumers will engage with online food shopping within 10 years. And it’s not just Millennials.  24% to 26% of Millennials ages 18-37 said they shop groceries online, 24% of Gen Xers say they buy online, and 21% of Baby Boomers say they buy groceries online. As detailed in point four, others have paved the way, and consumer habits are changing. Even the staying power of farmer’s markets points to a consumer willingness to deviate from the supermarket.
  6. Technology Is Ready
    For grocers to embrace this change, it goes without saying that they must adapt technologically to compete. The reality is that consumers want to purchase consumable goods, but on their terms and in their channel of choice. This highlights the importance of technologies like order management solutions that allow companies to move products quickly and efficiently, and eCommerce platforms that enable grocers to reach their customers where ever and whenever.

What happens next?

After nearly 100 years, the grocery industry is changing – arguably for the better – thanks to technologies that have made it possible not only to order a wider selection of groceries from the comfort of your home, but also to receive them at your convenience.

The grocery retailers that will survive will place a priority on responding to consumer’s needs across channels and touchpoints.

How will you participate in the grocery experience of the future?