Using Robotic Process Automation for competitor research

Successful businesses invest considerable time and resources in determining what their competitors are up to. What are they selling? What strategies and tactics have they implemented? What’s trending? Organizations need data and business intelligence to answer these important questions.

Robotic Process Automation (RPA) is a relatively new tool that offers a more efficient and cost-effective way to gather business intelligence than traditional market research techniques. Let’s take a look at its practical implications.

Read more: How RPA is changing your role in the workplace

RPA in a nutshell

When you implement RPA, you train “bots” to perform the same sequences of tasks that your employees perform on their computers. These aren’t physical robots like the ones on manufacturing assembly lines or on your favorite sci-fi show. Bots are snippets that wait for an email, online sale, alert, or other digital event, and then they compare their training instructions with the situation at hand to decide what to do.

For example, a bot can be trained to check incoming emails for purchase orders, automatically process orders under $1,000, and send larger orders to a person for approval. Bots accurately type, click, copy, paste, compare values, and navigate through web pages and other software up to fifteen times faster than humans… and they cost much less.

You can even integrate bots with voice and handwriting recognition, natural language processing engines, chatbots, machine learning solutions, and other software to expand their abilities into territory that was considered “humans-only” when this decade began. Your talent workforce will spend less time copying and collecting information, and will have more time to drive sales, craft creative business solutions, and perform other tasks that add increased value to your organization.

Using bots for competitor analysis

How does your business determine product pricing today? Among other factors, you need to discover the going rate for similar products on the market. Smaller companies that sell a handful of products might not find it difficult to monitor their competitors’ pricing. However, larger businesses that sell thousands of products and have ten, twenty, or even hundreds competitors can’t track their competition manually. RPA is an excellent solution for helping you obtain competitor data, optimize pricing, and improve profitability as changes arise.

Instead of directing a small team of employees to visit multiple competitors’ websites to determine what they’re selling and for how much, you can train a bot to do the same thing, except much faster. A single bot can compile as much competitor research in a day as a team of ten or more human workers. Since bots don’t need rest, they operate around the clock, which is especially useful for international organizations.

Using RPA eliminates the need for your talent workers to painstakingly compare your prices for hundreds of items against your competitors’. Bots can also identify products that are currently trending so you can source, manufacture, and market your wares to maximum effect.

The importance of choosing the right RPA implementation partner

Working with an experienced RPA implementation partner will help you quickly move from piloting your first bot to automating most of your organization’s repetitive manual tasks. Choose a team of experts that can help you extract the most benefit from your bots instead of offering half measures.

For example, a less experienced RPA partner might train a bot to email you whenever a product’s price changes on a competitor’s website, leaving you to compile reports by yourself. They might even collect competitors’ price updates in an Excel spreadsheet, but that still doesn’t take full advantage of what RPA offers. A competent RPA implementation partner will train bots to automatically send price updates to your ERP system or enterprise reporting solution, which will allow you to review price fluctuations and other trends via a dynamic dashboard.

Partnering with a technology provider that’s intimately familiar with your industry will help increase ROI and process efficiency. Visionet can help your organization build a strong foundation for future RPA implementations. For more information about implementing an RPA solution, contact Visionet for a complimentary session with one of our experts.

How signal processing can optimize non-seasonal product inventory

Winston owns and operates Winston’s Wristwatch Warehouse, a successful chain of stores dedicated to things that tick. They offer tens of thousands of high-end and mid-range watches from hundreds of international vendors. While Winston’s brand has been running like clockwork for years, he has noticed that several local competitors have begun to challenge his business. These rival watch merchants are now cutting into Winston’s share of the market.

How can Winston help his business prevail without sacrificing the quality and customer service that earned him his reputation?

Keeping your inventory lean

Winston’s answer lies in minimizing operating costs by optimizing his inventory. Most businesses severely limit their profitability by either spending too much on inventory or not stocking enough products to keep up with demand. The least successful among them are the ones that “follow their gut” and replenish their stock in an arbitrary and haphazard fashion. Others use previous years’ sales as a guide for reordering products. Even more statistically savvy businesses perform time-series analysis to estimate how many of each item to buy.

However, it’s no simple matter to accurately forecast changes in demand at each store for the thousands of products stocked by businesses like Winston’s. In this particular example, Winston’s flagship products are big-ticket items that cost thousands of dollars, have long lifespans, and lack a clear peak purchasing season. These characteristics make Winston’s business a poor fit for time-series analysis.

Worry not: digital signal processing can break this kind of complex pattern into simpler component patterns. Using signal processing for deep forecasting, data scientists can forecast demand with 60 percent less error than traditional statistical techniques.

Conclusion

Signal processing is a powerful technique that can be used in situations that aren’t suitable for more common time-series analysis. Combined with machine learning, this method can yield excellent accuracy for each product and store, even when sales data is incomplete.

To learn more about how Visionet’s inventory planning solution generates accurate demand forecasts using signal processing and machine learning, please download our white paper, “How to Slash Inventory Using Signal Processing & Machine Learning”.

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Striking gold with AcuitySpark

“Retail is detail.”

Chadatip Chutrakul, CEO of Siam Piwat, Thailand Thousands of customers walk in and out of your store or visit your website every single day. Do you know their likes and dislikes? What are their preferences in terms of products, colors, and styles? If you want to upsell or cross-sell products to them, do you know where to direct them? Are you aware of their buying power, their lifetime value, and whether or not they're likely to churn?

A clean, integrated customer dataset is a goldmine for retailers looking to improve marketing ROI and market share, and is key for retailers wanting to stay ahead of their competition. Retailers usually struggle to maintain what has popularly become known as a “Golden Record”. Studies have revealed that only 30 to 40 percent of customers are actually known to retailers unless a proper strategy is established and the necessary tools are put in place to maintain customer master data and convert unknown customers into known ones.

Even companies that have a strong CRM framework struggle to build a Golden Record because:

• Few attributes are typically available for unknown customers. This hinders the construction of a complete customer persona.
• In eCommerce, the segment of unknown customers is large due to the availability of the guest checkout option. As a result, every transaction is treated as one made by a new customer.

The figure below illustrates this problem graphically.

Given the hefty benefits of knowing your customer, customer relationship and marketing departments devise methods to identify customers and track their activity and transactions over time so they can move as many people as possible from quadrants 1 and 2 to quadrants 3 and 4. These methods include giving out loyalty cards, offering discounts on social media registration, and encouraging people to make purchases via mobile apps.

While these methods have their merits, they disregard historical data and transactions, and only work for customers that buy into these programs. A customer merge process can overcome both of these issues and create a Golden Record using the following method:
 A properly executed customer merge process can yield a significant improvement in the percentage of customers that are known.

 

AcuitySpark, Visionet’s proprietary solution for retail analytics, has a prebuilt module for merging customers that has been developed using top-of-the-line practices, tools, and technologies. Our clients have been able to attain Golden Records and move up to 45% percent of existing customers from quadrants 1 and 2 to 3 and 4 with minimum effort. Interested? Get in touch with Visionet today.

How can managed services address skill shortages?

Organizations of all sizes have begun to realize that they must digitalize their operations rapidly to maintain competitiveness. Whether they are upgrading from legacy technologies or venturing into the digital arena for the first time, their survival depends on successful digital transformation.

The biggest contributor to digital success is no longer access to capital. A recent poll of C-suite executives revealed that businesses are more concerned about technical resource shortages. Skill shortages are a clear and present threat to your organization’s digital future, but managed service providers (MSPs) provide a viable solution.

Here are six ways that MSPs address shortages of skilled resources:

• By proactively aligning your technology and business objectives

Continuing to rely on aging technologies and legacy platforms can generate unnecessary skill shortages. The older a technology is, the smaller and more expensive its relevant talent pool will be. If you find yourself in desperate need of a COBOL or FORTRAN developer, there’s a strong possibility that your IT budget would be better spent on platform modernization.

Instead of blindly providing support for your existing IT infrastructure, MSPs continually evaluate and suggest improvements to your digital systems. If there are obvious ways that newer technology can reduce operating costs or generate more revenue, your MSP will let you know. You might not need to hire those expensive legacy platform consultants after all.

• By continually upgrading resources’ skills

As an important corollary to section 1, managed services also helps you avoid skill stagnation.

Suppose you rely solely on your in-house IT department. In this scenario, you need to keep an eye on technology trends to determine when to upgrade your staff’s technical skills. If you fail to do so, then your support staff will remain stuck in the past. This will either hold back progress because you’re reluctant to modernize your digital infrastructure, or it will create new skill gaps if you decide to go ahead with system upgrades. You’ll also be responsible for all training and certification costs.

Your MSP, on the other hand, is responsible for ensuring that their teams are sufficiently trained and experienced, no matter which technology platform you currently use. If your business applications are upgraded or replaced, then it’s your MSP’s responsibility to ensure that the team assigned to you is fully capable of supporting them.

• By sharply reducing resource onboarding time

Sometimes you need to move quickly to capitalize on a lucrative opportunity, but you don’t have the right personnel to get the job done. You could try to find experts with the right skill set and experience in your specific industry, but that might take weeks or months. By the time new resources sign on the dotted line, your window of opportunity would be long gone. You can’t retrain your existing team overnight, either.

Having access to an MSP opens up new avenues for increasing operational agility. Instead of waiting to find and onboard a new skilled resource, your MSP can quickly assign the perfect expert from their large, elastic resource pool. By engaging your MSP’s specialists, your new project can get started in a matter of days. Your MSP will scale down your assigned team once the project is complete, providing higher ROI and lower TCO.

• By providing affordable access to large teams of skilled resources

Highly skilled resources don’t come cheap, and so many companies only have one or two experts on their payroll for specific mission-critical technologies. This is a fairly high-risk strategy. If your resident expert becomes unavailable for an extended period, your organization becomes exposed to business risk. In the event of a service disruption, you would be forced to seek expensive, per-incident technical support by an external organization with a limited understanding of your IT infrastructure.

Reliable MSPs have large teams of experts on their roster who can be assigned to support your applications, hardware, and other digital resources. That way, your business continuity doesn’t rely on the availability of a single expert and you don’t need a large team of highly paid experts on your payroll to manage each major component of your technology footprint.

• By supporting 24x7x365 global operations

If your organization maintains operations in multiple time zones, then having a large support team is especially important. Most MSPs offer remote support options with resources stationed in different international regions. The size of their team and their geographical coverage allow them to provide high-quality, around-the-clock support at a lower price point than you can offer in-house.

• By providing fully managed, flexible team composition

As your organization’s mix of digital tools evolves, so do your support requirements. For example, if you decide to start an enterprise-wide project to optimize your existing digital operations, your existing support crew might not be the best fit for the job. So do you hire new resources on short-term contracts for the duration of the optimization project?

MSPs provide a better solution. Since managed services are proactive in nature, your MSP will seamlessly manage the transition from a support-focused to an optimization-focused team, and then transition back to a support focus when the project is complete. Any adjustment in their team makeup is just another aspect of their full-service approach to addressing your digital needs.

Conclusion

MSPs provide large, agile, and flexible teams of highly trained experts to address your organization’s digital needs. They offer great value by taking on the risks and responsibilities associated with skill gaps and evolving business needs. Partnering with an MSP frees you from many of the complexities associated with recruiting and maintaining your own team of highly compensated technology experts.

To learn more about how managed services can help you optimize your workforce, please contact Visionet.

How is machine learning making vending machines smarter?

Machine learning is all about recognizing subtle patterns in data and then extracting important insights to solve complex problems. Machine learning helps organizations change generic, one-size-fits-all processes into efficient, contextually sensitive processes that save time, effort, and money.

Vending machine management is an example of a set of processes that machine learning can analyze and enhance. The vending machine industry represents a huge opportunity, with as many as 31.6 million machines in operation and a market size of over $30 billion by 2025. Real-time vending machine data collection and analysis is becoming easier with the introduction of connected vending machines, which are expected to exceed 3.6 million by next year.

In this blog post, we’ll discuss how machine learning can give forward-thinking vending machine businesses a massive competitive advantage over their competitors.

Machine-specific resupply

People in different countries and cities tend to favor different snacks and beverages. Even within the same city, SKUs that sell well in a vending machine in a movie theater might not perform as well in a vending machine in a gym. If vending machine user preferences vary from location to location, why should vending machine businesses stock each machine identically?

You can apply machine learning techniques to your company-wide vending machine sales data to determine the best product assortment for each machine. With more accurate demand forecasting for each product and location, your machines won’t run out of popular items between deliveries or carry as many products that aren’t selling.

Addressing seasonal demand

Your customers’ preferences change depending on the time of day, day of the week, and the weather. Even if each of your machines is stocked with the right product assortment for its customers, that assortment should be adjusted from resupply to resupply to match these gradual shifts in demand.

Machine learning techniques like time series analysis can identify annual, monthly, weekly, or other cycles of rising and falling demand for each product. You might discover that people switch to cheaper snacks towards the end of the month, or they might prefer chocolate over potato chips in the winter. The great thing about machine learning is that you don’t need to come up with an explanation for why people’s preferences change; you just need to follow the data.

Optimizing resupplies

Taken to the next level, machine learning can help you find an optimal replenishment schedule for each of your vending machines. Some might need to be restocked once a week, while others might need a refill every other day. The goal is to optimize sales against resupply costs.

Even though your stock will vary from shipment to shipment, sophisticated planning software powered by machine learning can tell you how much of each product to load onto each truck. Planning software can also find the best route for each of your delivery trucks as they wind through your network of vending machines. Your drivers will receive delivery schedules that help them restock more machines using less time and fuel.

Machine learning can even help identify the best time to visit each machine; a time of low demand for that machine (to minimize lost sales during downtime) and low traffic on the truck’s delivery route (to minimize time and fuel costs).

Perfecting your plan-o-grams

You can even apply machine learning on a more fine-grained scale. Analysis of sales across your vending machine network can help you improve revenue by adjusting each machine’s plan-o-gram. Some products might require more than one facing. You might learn that people unconsciously prefer items from a specific row or column. Positioning competing or complementary items next to each other might positively or negatively influence buying decisions. Machine learning can suggest changes that influence people to spend more, but are too subtle for human decision-makers to identify unaided.

Better long-term product planning

Machine learning can also guide your company’s overall product offering. Non-seasonal drops in demand might suggest that a specific product has begun to fall out of favor. This information can help you phase these products out of your catalog before they become dead stock.

You can also use non-seasonal increases in demand to identify the types of product that are gaining popularity. Techniques like collaborative filtering can provide recommendations for similar products that your customers might also like.

Smart vending machines and personalization

Newer “smart” vending machines allow users to pay for products using their smartphone. In addition to providing customers more convenient payment methods, this also enables vending machine businesses to send users personalized product recommendations and special offers. Market basket analysis is a machine learning technique that uses a customer’s purchase history to determine their unique preferences and generate relevant suggestions for their next purchase.

The ability to send targeted messaging to an existing customer is a very powerful advertising channel that turns a “passive” vending machine that waits for customers into an “active” virtual salesperson that lets your customers know what’s new and what they might enjoy.

Conclusion

Machine learning has become an affordable tool that businesses can use to improve efficiency and align their products and services more closely with real-world demand. Vending machine operators can use machine learning to refresh their overall product line and tailor individual machines’ stock according to hyperlocal and seasonal differences in customer preferences.

Machine learning can optimize vending machine replenishment schedules and delivery routes, and can also adjust machines’ plan-o-grams to gently convince customers to spend more. A global beverage brand recently increased revenue by 6% and reduced restocking trips by 15% by adopting machine learning. Smartphone-enabled vending machines can even generate demand by pushing highly personalized messaging to their customers.

Vending machines are just one example of how machine learning is revolutionizing the retail industry. For more information on how you can apply these techniques to your own business, please contact Visionet.

Why is eCommerce integration important?

A decade or two ago, eCommerce might have seemed to be reserved for large corporations with annual revenues in the hundred millions. For the past several years, however, the barriers to entry for online commerce are so low and the potential returns so high that establishing an online store should be an integral part of virtually every business plan. Opting for pure conventional commerce is no longer viable.

So you’ve either already set up an online storefront or are actively pursuing a digital commerce solutions. That’s great, but is it enough?

The woes of digital solitude

You might discover that their eCommerce platform is a rather solitary creature that doesn’t play well with the rest of your organization’s digital ecosystem. It might have its own set of reports and dashboards, separate from reports on your financials and internal operations… or it might not have these reports at all. The former scenario is far from ideal, while the latter is simply unacceptable.

In addition to reconciling reports from multiple sources, your staff will probably spend several hours each week manually rekeying customer and product information between your eCommerce platform and other information systems like your ERP platform, warehouse management software, or shipping solution. Those lost hours would be better spent closing sales or serving customers.

Disconnected eCommerce and warehouse management systems slow down your pay-to-order cycle. The longer it takes to process an order and ship the right product, the higher your cost will be per order. Factor in the errors resulting from manual entry and the potential costs are even higher. If your order processing workflow involves email or spreadsheets, then there is ample room for improvement.

Using a standalone eCommerce system also makes it trickier to inform online shoppers about product availability. If your inventory management solution isn’t integrated with your online store, how will shoppers know whether the items they want are in stock?

The cross-channel conundrum

For most retailers, however, eCommerce is just one slice of their sales pie. Your business probably has old-fashioned brick-and-mortar stores, a wholesale business, or even telephone shopping where customers choose products out of a printed catalog.

Bringing together current and accurate information from each of these channels is extremely useful. You can enable cross-channel fulfillment, allowing customers to pay using one channel, receive items using a second, and return items using a third. You can maintain a shared inventory so that your customers have access to your complete product catalog no matter how they choose to shop. You can also provide consistently excellent customer service by sharing customers’ complete support and order history across call centers, retail locations, and fulfillment centers.

Without digital integration across eCommerce, supply chain, and finance, efficient cross-channel fulfillment, inventory, and support is virtually impossible. Many businesses maintain separate product catalogs, customer records, and delivery information for their various retail channels. They spend a lot of time asking their customers to repeat their personal details and reason for calling. They miss sale opportunities because some products aren’t available via a particular channel. They require customers to return to their point of purchase for exchanges and refunds. In short, they fail to offer their customers excellent customer service.

Integration using a pre-built integration solution

Integrating eCommerce with the rest of your business systems is a great investment, but there’s more than one way to achieve unified commerce. Some retailers embark on custom enterprise integration projects, which tend to be expensive and time-consuming. However, it’s possible to achieve the same results more affordably and in less time using ready-made integration solutions designed specifically for eCommerce platforms.

While pre-built integration solutions usually offer better ROI than custom integration, there are a few important factors to keep in mind.

Get an integration solution that supports bidirectional integration. If you want to synchronize business information between Magento and Microsoft Dynamics 365, for example, the integration solution needs to send order information from Magento to Dynamics 365 and inventory information from Dynamics 365 to Magento to be truly effective.

Make sure that the integration solution deploys quickly. It should be compatible with your eCommerce platform and ERP system to enable rapid integration out of the box. Since more and more digital business solutions like eCommerce and ERP platforms are moving to the cloud, the solution should support cloud-based solutions.

You should choose an integration solution by a provider that offers complete implementation and support services. Ideally, their operational model should include both onshore and offshore components to give you an optimal mix of responsiveness and cost-effectiveness.

Conclusion

eCommerce has become an essential part of every successful business, but setting up an online store is only half the battle. It is just as important to establish automatic communication between your eCommerce system and other digital business solutions like your ERP system and warehouse management solution. For more information on how a pre-built eCommerce integration solution can improve operational efficiency with optimal time to value, please join us for our webinar on February 27, “Making Omni-Channel Real in B2B and B2C Commerce, Unifying Magento with Dynamics 365”.

Swimming with the Digital Commerce Sharks

Amazon is a leader in eCommerce and offers unmatched value to its customers with its 2-day and next-day delivery promise. Amazon even extends its 2-day shipping service to other sellers using “Amazon Shipping”. Amazon can provide the 2-day delivery promise using its extended network of distribution and fulfillment centers across the country. For any other B2C or B2B company to match this level of guarantee would mean a huge investment for which they can’t leverage the economies of scale that Amazon can.

While there is no guessing how far Amazon’s monopoly in eCommerce and fulfillment will grow, there is something other businesses can do. But in this scenario of “everyone else versus Amazon”, sellers, distributors, and shipping carriers will have to join forces, so to speak, to thwart or at least match Amazon on its promise.

The first step in this process is for organizations to understand how they can expand their existing network, either utilizing their own facilities or through partnerships.

1. Brick and mortar retailers who have extended their retail store networks have to look inwards, leverage their real estate holdings, and adopt a model where the retail stores are used as distribution centers to fulfill online orders.


2. Businesses can utilize 3PL/4PL services to extend their network to areas with no current physical presence. There are many options available in this space and more startups are coming up with tailored value-add for 2-day shipping.


3. Big-box stores have an existing network of distribution centers and stores that can be of huge value to many smaller eCommerce businesses. Big-box stores can become part of the solution by opening their distribution networks and letting sellers leverage these extensive networks as well to provide faster deliveries, returns, and customer support.

For this model to succeed, adjustments have to be made to supply chain operations, merchandizing, forecasting, and replenishment processes. These changes have to be driven by technology in a bid to flexibly automate as much of their operations as possible.

• Internal stores should be empowered to run pick, pack, and ship operations. A decent WMS solution capable of automatically manifesting to common shipping carriers and rate shopping for specific delivery is a must-have.
• Robust integration is required to succeed in partnerships with 3PL/4PL and 2-day fulfillment startups.
• Establish a process to automatically route online orders to the nearest fulfillment center (own or partner). Modern distributed order management (DOM) features handle order routing with decent accuracy. Establishing and maintaining zones and defining distribution priorities is the first step towards putting together a DOM system.
• An agile demand and inventory planning, allocation, and replenishment process is fundamental for such an operational shift. Machine learning and artificial intelligence needs to lead the way in approximating true weekly demand and feeding to weekly distribution planning and replenishment from main distribution centers down to fulfillment centers. This process is key to the success of the operation, and it has to be accurate and automatic.
• One big internal challenge in true omni-channel adoption is achieving consensus on how revenue centers are defined and how profitability is measured for online versus retail. If retail stores are acting as fulfillment centers, they are essentially consuming their sellable stock and there is no easy way to trace sales lost due to stockouts or unavailability of specific SKUs. One way to make this kind of model successful is to look at cross-channel revenue and profits separately for each store. Stores can also see their individual numbers go up with the success of the operation and maintain higher levels of safety stock at store locations instead of stocking only at the main distribution centers.

Amazon is big, it’s growing, and it’s here to stay. So is 2-day shipping. Sellers, distributors, and retailers must think creatively to counter the looming threat to their business. In order to survive and thrive, competitors must beat Amazon at its own game.