Geschriebene Artikel über Big Data Analytics

Tehreem Naeem

Simplify Vendor Onboarding with Automated Data Integration

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Vendor onboarding is a key business process that involves collecting and processing large data volumes from one or multiple vendors. Business users need vendor information in a standardized format to use it for subsequent data processes. However, consolidating and standardizing data for each new vendor requires IT teams to write code for custom integration flows, which can be a time-consuming and challenging task.

In this blog post, we will talk about automated vendor onboarding and how it is far more efficient and quicker than manually updating integration flows.

Problems with Manual Integration for Vendor Onboarding

During the onboarding process, vendor data needs to be extracted, validated, standardized, transformed, and loaded into the target system for further processing. An integration task like this involves coding, updating, and debugging manual ETL pipelines that can take days and even weeks on end.

Every time a vendor comes on board, this process is repeated and executed to load the information for that vendor into the unified business system. Not just this, but because vendor data is often received from disparate sources in a variety of formats (CSV, Text, Excel), these ETL pipelines frequently break and require manual fixes.

All this effort is not suitable, particularly for large-scale businesses that onboard hundreds of vendors each month. Luckily, there is a faster alternative available that involves no code-writing.

Automated Data Integration

The manual onboarding process can be automated using purpose-built data integration tools.

To help you better understand the advantages, here is a step-by-step guide on how automated data integration for vendor onboarding works:

  1. Vendor data is retrieved from heterogeneous sources such as databases, FTP servers, and web APIs through built-in connectors available in the solution.
  2. The data from each file is validated by passing it through a set of predefined quality rules – this step helps in eliminating records with missing, duplicate, or incorrect data.
  3. Transformations are applied to convert input data into the desired output format or screen vendors based on business criteria. For example, if the vendor data is stored in Excel sheets and the business uses SQL Server for data storage, then the data has to be mapped to the relevant fields in the SQL Server database, which is the destination.
  4. The standardized, validated data is then loaded into a unified enterprise database that you can use as the source of information for business processes. In some cases, this can be a staging database where you can perform further filtering and aggregation to build a consolidated vendor database.
  5. This entire ETL pipeline (Step 1 through Step 4) can then be automated through event-based or time-based triggers in a workflow. For instance, you may want to run the pipeline once every day, or once a new file/data point is available in your FTP server.

Why Build a Consolidated Database for Vendors?

Once the ETL pipeline runs, you will end up with a consolidated database with complete vendor information. The main benefit of having a unified database is that it would have filtered information regarding vendors.

Most businesses have a strict process for screening vendors that follows a set of predefined rules. For example, you may want to reject vendors that have a poor credit history automatically. With manual data integration, you would need to perform this filtering by writing code. Automated data integration allows you to apply pre-built filters directly within your ETL pipeline to flag or remove vendors with a credit score lower than the specified threshold.

This is just one example; you can perform a wide range of tasks at this level in your ETL pipeline including vendor scoring (calculated based on multiple fields in your data), filtering (based on rules applied to your data), and data aggregation (to add measures to your data) to build a robust vendor database for decision-making and subsequent processes.

Conclusion

Automated vendor onboarding offers cost-and-time benefits to your organization. Making use of enterprise-grade data integration tools ensures a seamless business-to-vendor data exchange without the need for reworking and upgrading your ETL pipelines.

Benjamin Aunkofer

Interview – Predictive Maintenance and how it can unleash cost savings

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Interview with Dr. Kai Goebel, Principal Scientist at PARC, a Xerox Company, about Predictive Maintenance and how it can unleash cost savings.

Dr. Kai Goebel is principal scientist as PARC with more than two decades experience in corporate and government research organizations. He is responsible for leading applied research on state awareness, prognostics and decision-making using data analytics, AI, hybrid methods and physics-base methods. He has also fielded numerous applications for Predictive Maintenance at General Electric, NASA, and PARC for uses as diverse as rocket launchpads, jet engines, and chemical plants.

Data Science Blog: Mr. Goebel, predictive maintenance is not just a hype since industrial companies are already trying to establish this use case of predictive analytics. What benefits do they really expect from it?

Predictive Maintenance is a good example for how value can be realized from analytics. The result of the analytics drives decisions about when to schedule maintenance in advance of an event that might cause unexpected shutdown of the process line. This is in contrast to an uninformed process where the decision is mostly reactive, that is, maintenance is scheduled because equipment has already failed. It is also in contrast to a time-based maintenance schedule. The benefits of Predictive Maintenance are immediately clear: one can avoid unexpected downtime, which can lead to substantial production loss. One can manage inventory better since lead times for equipment replacement can be managed well. One can also manage safety better since equipment health is understood and safety averse situations can potentially be avoided. Finally, maintenance operations will be inherently more efficient as they shift significant time from inspection to mitigation of.

Data Science Blog: What are the most critical success factors for implementing predictive maintenance?

Critical for success is to get the trust of the operator. To that end, it is imperative to understand the limitations of the analytics approach and to not make false performance promises. Often, success factors for implementation hinge on understanding the underlying process and the fault modes reasonably well. It is important to be able to recognize the difference between operational changes and abnormal conditions. It is equally important to recognize rare events reliably while keeping false positives in check.

Data Science Blog: What kind of algorithm does predictive maintenance work with? Do you differentiate between approaches based on classical machine learning and those based on deep learning?

Well, there is no one kind of algorithm that works for Predictive Mantenance everywhere. Instead, one should look at the plurality of all algorithms as tools in a toolbox. Then analyze the problem – how many examples for run-to-failure trajectories are there; what is the desired lead time to report on a problem; what is the acceptable false positive/false negative rate; what are the different fault modes; etc – and use the right kind of tool to do the job. Just because a particular approach (like the one you mentioned in your question) is all the hype right now does not mean it is the right tool for the problem. Sometimes, approaches from what you call “classical machine learning” actually work better. In fact, one should consider approaches even outside the machine learning domain, either as stand-alone approach as in a hybrid configuration. One may also have to invent new methods, for example to perform online learning of the dynamic changes that a system undergoes through its (long) life. In the end, a customer does not care about what approach one is using, only if it solves the problem.

Data Science Blog: There are several providers for predictive analytics software. Is it all about software tools? What makes the difference for having success?

Frequently, industrial partners lament that they have to spend a lot of effort in teaching a new software provider about the underlying industrial processes as well as the equipment and their fault modes. Others are tired of false promises that any kind of data (as long as you have massive amounts of it) can produce any kind of performance. If one does not physically sense a certain modality, no algorithmic magic can take place. In other words, it is not just all about the software. The difference for having success is understanding that there is no cookie cutter approach. And that realization means that one may have to role up the sleeves and to install new instrumentation.

Data Science Blog: What are coming trends? What do you think will be the main topic 2020 and 2021?

Predictive Maintenance is slowly evolving towards Prescriptive Maintenance. Here, one does not only seek to inform about an impending problem, but also what to do about it. Such an approach needs to integrate with the logistics element of an organization to find an optimal decision that trades off several objectives with regards to equipment uptime, process quality, repair shop loading, procurement lead time, maintainer availability, safety constraints, contractual obligations, etc.

True Tamplin

Conversion Rate Optimization: Understanding the Sales Funnel

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Are you capturing the attention of consumers or prospects with your content? Do they trust you enough to give you their contact information? Will they come back and buy from you again? Knowing how the sales funnel works and what you can do to improve it will take you down the road of success.

Business 101

As a business owner, your goal is to turn a prospect (meaning a prospective buyer) into a loyal customer. Nobody wants to lose a possible customer after putting a lot of effort into the attempt of establishing a relationship. Once you understand the different stages of the sales funnel, it will be easier to find cracks and holes within. The following sections unpack how sales funnel management can help you optimize your conversion rate and build a successful long-term relationship with your customers and website users.

The Sales Funnel

The sales funnel describes the path a customer takes on the way to buying a product or service. It visualizes the typical journey they go through and in which stage of the buying decision prospects are at the moment. As one of the core concepts in digital marketing, sales funnel management can help you to understand your audience and prevent them from dropping out before a sale is made. It is about giving every potential customer the treatment they are looking for. If you don’t understand your sales funnel, you can’t optimize it. What matters most when it comes to a sales funnel is website optimization.

Prospects move from the top of the funnel to the bottom as they become more familiar with what you have to offer. The sales funnel narrows as visitors move through it, and the number of people in your funnel will continue to decrease the closer you get to sealing the deal. It starts at the top with all the prospects who landed on your website one way or another, while the narrow bottom represents loyal customers.

The 4 Stages of the Sales Funnel

Moving people through the funnel can be a challenge. A stratagem to keep in mind is that your goal should be to solve the “problems” of your customers, or potentially make them aware of a problem they didn’t even know existed. Start by creating content that attracts your prospect’s attention, followed by offering an irresistible solution to the problem. All you have to do then is watch the magic happen.

Truthfully, that is easier said than done, but if you follow the four stages of a prospective customer’s mindset, you will reach your goal sooner than later. The different stages can be easily explained using the AIDA (Awareness, Interest, Decision, Action) strategy. To understand what moves a buying decision, we have to take a closer look at each stage and the approach it requires.

Awareness

To end up with a strong bond with your prospect, you have to gain attention first. Depending on how they found you (organic search results, recommendations, advertisements, or just pure luck), people will put different amounts of trust in your business. If you are lucky and all circumstances fall perfectly into place, a prospect turns into a customer immediately. More often though, the awareness stage does exactly what it sounds like; it creates awareness of your business and your products or services. At this point, all you are trying to do is lead prospects into the next stage, which will make them return for more.

Interest

Once a potential customer is aware of you, you need to build their interest. In this stage potential customers are interested in what you have to offer and are doing research or comparison. It is the perfect time to show off authority in your field and support them with helpful content that does not yet try to sell to them. Make sure your message stays consistent throughout the whole process and do not try to push too hard from the beginning. The interest stage should only lead them to be able to make an informed decision.

Decision

For the most part, the majority of people do not like making decisions and, therefore, getting a prospect to make a buying decision is not an easy feat. At this stage, you have to bring on your A-game and make them an offer they can’t refuse. Whether this means offering free premium shipping, a discount code, or a free month of your services is totally up to you; you just have to make sure that your potential customer wants to take advantage of it. Showcasing positive reviews or social proof is another powerful way that you can get people to take action.

 Action

Now your prospect turns into a customer. When he or she purchases your product or takes advantage of your service, that customer becomes part of your business’s ecosystem. But just because they reached the final stage of the sales funnel and the AIDA principle doesn’t mean your work is all said and done. Starting to build a long-term relationship with someone who already trusts your company is easier than starting the sales funnel all over again with a new prospect.

Sales Funnel Management

At this point, you should understand why sales funnel management is so important. Even the best prospects can get lost along the way if expectations aren’t met. It takes time to build a sales funnel that represents what your audience is looking for. The best way to optimize a sales funnel is to start with the results and work your way up. Another point of interest is the timing when people move from one point to the next within the funnel. This can help you find out where, when, and why you’re losing potential customers.

Too slow: New leads are nine times more likely to convert if someone follows up within the first five minutes. On the other hand, a lead is 21 times less likely to turn into a sale after 30 minutes have passed. To react within tight response times like that, you need to implement sales funnel management automation.

Too impatient: It can be tempting to dump a lead that isn’t converting right away and move on to the next. You should ask yourself the question if you are patient enough and if you are following up as much as you should. A marketing automation funnel also helps to stay in touch with the prospect over time.

Too fast: Instead of asking people to buy from you right away, you should cultivate them over time. If you adjust your sales approach to the different stages, you don’t just avoid chasing them away; you also find out what is working and what is a waste of your time.

How can you optimize your conversion rate?

There are countless ways you can improve your conversion rate and turn a “no, thank you” into a “yes, please.” In sales, a no often simply means “not until later” or “try again, I’m just not totally convinced yet.” Any time you encounter problems like that, you can use one or multiple of the following, mostly automated sales techniques, to reach your goals.

Target your Audience

To lead people into your sales funnel, you have to put the right content in front of your prospects. How and where you do that depends on your target audience. Be creative with your content, but make sure it mimics your offer and the call-to-action you are using. Customer relationship management (CRM) can help you track interactions with current and future customers.

Build a Landing Page

A landing page offers content that addresses a specific problem, ideally with a single call-to-action, and should steer your visitor towards becoming a customer. A/B testing your landing pages will help you figure out what your audience responds to best and what language, imagery, or layouts can help you improve conversion rates. Experienced hosting companies like 101domain can help you along the way. Additionally, you can use pay-per-click campaigns to drive traffic to your landing page and contact forms to gain subscribers to a mailing list.

Targeting Soft Conversions

When considering which page to use as a landing page, you can increase your conversion rate by bringing leads to an on-site resource to gain a “soft conversion.”

 To illustrate the importance of a good landing page and soft conversions, consider the following data:

RED: Cost per conversion BLUE: Number of conversions X-AXIS: Time (Screenshot supplied by Howard Ahmanson)

The initial strategy represented in this graph was to take visitors directly to a sales page. This resulted in a very low number of conversions, about a rate of 1%,, which in turn drove the cost per conversion way up. Later, the landing page was switched to an on-site resource, such as  a form fill of “get the free retirement planning guide.” This prompted a few soft conversions, or in other words email addresses. Upon doing this, the average number of conversions per month increased from about 10 to between 30 and 45, which in turn dropped the total cost per conversion from a median of about $400 to about $100. This is an approximately 300% increase in conversions at 50% of the cost.

But how does increased conversions translate in terms of sales numbers? To see an example of this, consider the data from the Ken Tamplin Vocal Academy:

RED: Total conversion, including soft conversions
BLUE: Sales conversions
X-AXIS: Time

When running ads for Ken, the initial strategy was to bring prospects directly to a sales page. Later, this was switched out for a “Yes! I want Ken’s free lessons!” page.

This led to an increase in the number of soft conversions, which led to a tightly correlated increase in sales. There was an increase from around 30 conversions per month up to over 225, which is an increase of 750%.

Create an Email Drip Campaign

Email drip campaigns are used to send a pre-written set of emails to subscribers or customers over time. You can use those campaigns to educate the receiver as well as make them aware of sales or offers. Last but not least, don’t forget about existing customers. This technique is ideal for building up loyalty and making them feel like part of the family.

Michael Lyamm

Optimize AI Talent: Perception from Across the Globe

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Despite the AI hype, the AI skill gap is turning into some pariah while businesses are accelerating to become demigods.

Reports from the “Global Talent Competitiveness Index (GTCI) 2020” cover multiple parameters both national and organizational to generate insight for further action. This report compiles 70 variables including 132 national economies across the globe – based on all groups of income and at every developmental level.

The sole purpose of the GTCI report is to narrow down the skill gap by delivering the right data inputs. The figures mentioned in the report could be of value to private and public organizations.

GTCI report covered multiple themes that need to be addressed: –

As the race to embrace AI spurs, it is evident to address the challenges faced due to AI and how best these problems can be solved.

The pace at which AI is developing is transforming the way we work, forcing a technology shift, change in the corporate structure, changing the innovation system for AI professionals in every possible way.

There’s more that is needed to be done as AI and automation continue to affect the way we work.

  • Reskilling in workplaces to eliminate dearth of talent

As the role in AI keeps evolving, organizations need a larger workforce, especially to play technology roles such as AI engineers and AI specialists. Looking closely at the statistics you may not fail to notice that the number of AI job roles is on the rise, but there’s scarce talent.

Employers must take on reskilling as a critical measure. Else how will the technology market keep up with changing trends? Reskilling in the form of training or AI certifications should be emphasized. Having an in-house AI talent is an added advantage to the company.

  • Skill gap between growing countries (low performing and high performing) are widening

Based on the GTCI report, it is seen there is a skill gap happening not only across industries but between nations. The report also highlights which country lacks basic digital skills, and this highly gets contributed toward a digital divide between nations.

  • High-level of cooperation needed to embrace AI benefits

As much as the world shows concern toward embracing AI, not much has been done to achieve these transformations. And AI has huge potential to transform society and make it a better place to live. However, to embrace these benefits, corporations must engage in AI regulation.

From a talent acquisition perspective, this simply means employers will need more training and reskilling opportunities.

  • AI to allow nations to skip generations

On a technological front, AI makes it possible to skip generations in developed nations. Although, not common due to structural obstruction.

  • Cities are now competing to become talent magnets and AI hubs

As AI continues to hit the market, organizations are aggressively coming up with newer policies to attract and retain AI professionals.

No doubt, cities are striving to attract the right kind of talent as competition keeps increasing. As such many cities are competing in becoming core AI engines in transforming energy grids, transportation, and many other multiple segments. Cities are now becoming the main test beds for AI-based tools i.e. self-driven vehicles, tele-surveillance, and facial recognition.

  • Sustainable AI comes when the society is equally up for it

With certain communities not adopting and accepting the advent of AI, it is difficult to say whether these communities will not try to distort AI narratives. As a result, it is crucial for multiple stakeholders to embrace AI and developed the AI workforce in parallel.

Not to forget, regulators and policy-makers have an equal role to play to ensure there’s a smooth transition in jobs. As AI-induced transformation skyrockets, educators and leaders need to move quickly as the new generations’ complete focus is entirely based on doing their bit to the society.

Two decades passed ever since McKinsey declared the war for talent – particularly for high-performing employees. As organizations are extensively looking to hire the right talent, it is imperative to retain and attract talent at large.

Despite the unprecedented growth in AI technologies, it is near to being unanimous regarding having hold of organizations to master in AI, forget about retaining talent. They’re not even getting better at it.

Even top tech companies such as Google and Amazon, the demand for top talent outstrips the supply. Although you may find thousands of candidates applying for the same job role, the competition just gets tougher since such employers are tough nuts and pleasing them is not an easy task.

If these tech giants are finding it difficult to hire the right talent, you could imagine the plight of other companies.

Given the optimistic view regarding the technology future, it is much more challenging to convince that the war for talent truly resembles the war on talent.

The good news is organizations that look forward to adopting new technology and reskill their employees will most likely thrive in the competitive edge.

Tap London

Customer Journey Mapping: The data-driven approach to understanding your users

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Businesses across the globe are on a mission to know their customers inside out – something commonly referred to as customer-centricity. It’s an attempt to better understand the needs and wants of customers in order to provide them with a better overall experience.

But while this sounds promising in theory, it’s much harder to achieve in practice. To really know your customer you must not only understand what they want, but you also need to hone in on how they want it, when they want it and how often as well.

In essence, your business should use customer journey mapping. It allows you to visualise customer feelings and behaviours through the different stages of their journey – from the first interaction, right up until the point of purchase and beyond.

The Data-Driven Approach 

To ensure your customer journey mapping is successful, you must conduct some extensive research on your customers. You can’t afford to make decisions based on feelings and emotions alone. There are two types of research that you should use for customer journey mapping – quantitative and qualitative research.

Quantitative data is best for analysing the behaviour of your customers as it identifies their habits over time. It’s also extremely useful for confirming any hypotheses you may have developed. That being so, relying solely upon quantitative data can present one major issue – it doesn’t provide you with the specific reason behind those behaviours.

That’s where qualitative data comes to the rescue. Through data collection methods like surveys, interviews and focus groups, you can figure out the reasoning behind some of your quantitative data trends. The obvious downside to qualitative data is its lack of evidence and its tendency to be subjective. Therefore, a combination of both quantitative and qualitative research is most effective.

Creating A Customer Persona

A customer persona is designed to help businesses understand the key traits of specific groups of people. For example, those defined by their age range or geographic location. A customer persona can help improve your customer journey map by providing more insight into the behavioural trends of your “ideal” customer. 

The one downside to using customer personas is that they can be over-generalised at times. Just because a group of people shares a similar age, for example, it does not mean they all share the same beliefs and interests. Nevertheless, creating a customer persona is still beneficial to customer journey mapping – especially if used in combination with the correct customer journey analytics tools.

All Roads Lead To Customer-centricity 

To achieve customer-centricity, businesses must consider using a data-driven approach to customer journey mapping. First, it requires that you achieve a balance between both quantitative and qualitative research. Quantitative research will provide you with definitive trends while qualitative data gives you the reasoning behind those trends. 

To further increase the effectiveness of your customer journey map, consider creating customer personas. They will give you further insight into the behavioural trends within specific groups. 

This article was written by TAP London. Experts in the Adobe Experience Cloud, TAP London help brands organise data to provide meaningful insight and memorable customer experiences. Find out more at wearetaplondon.com.

Aakash Chugh

How Important is Customer Lifetime Value?

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This is the third article of article series Getting started with the top eCommerce use cases. If you are interested in reading the first article you can find it here.

Customer Lifetime Value

Many researches have shown that cost for acquiring a new customer is higher than the cost of retention of an existing customer which makes Customer Lifetime Value (CLV or LTV) one of the most important KPI’s. Marketing is about building a relationship with your customer and quality service matters a lot when it comes to customer retention. CLV is a metric which determines the total amount of money a customer is expected to spend in your business.

CLV allows marketing department of the company to understand how much money a customer is going  to spend over their  life cycle which helps them to determine on how much the company should spend to acquire each customer. Using CLV a company can better understand their customer and come up with different strategies either to retain their existing customers by sending them personalized email, discount voucher, provide them with better customer service etc. This will help a company to narrow their focus on acquiring similar customers by applying customer segmentation or look alike modeling.

One of the main focus of every company is Growth in this competitive eCommerce market today and price is not the only factor when a customer makes a decision. CLV is a metric which revolves around a customer and helps to retain valuable customers, increase revenue from less valuable customers and improve overall customer experience. Don’t look at CLV as just one metric but the journey to calculate this metric involves answering some really important questions which can be crucial for the business. Metrics and questions like:

  1. Number of sales
  2. Average number of times a customer buys
  3. Full Customer journey
  4. How many marketing channels were involved in one purchase?
  5. When the purchase was made?
  6. Customer retention rate
  7. Marketing cost
  8. Cost of acquiring a new customer

and so on are somehow associated with the calculation of CLV and exploring these questions can be quite insightful. Lately, a lot of companies have started to use this metric and shift their focuses in order to make more profit. Amazon is the perfect example for this, in 2013, a study by Consumers Intelligence Research Partners found out that prime members spends more than a non-prime member. So Amazon started focusing on Prime members to increase their profit over the past few years. The whole article can be found here.

How to calculate CLV?

There are several methods to calculate CLV and few of them are listed below.

Method 1: By calculating average revenue per customer

 

Figure 1: Using average revenue per customer

 

Let’s suppose three customers brought 745€ as profit to a company over a period of 2 months then:

CLV (2 months) = Total Profit over a period of time / Number of Customers over a period of time

CLV (2 months) = 745 / 3 = 248 €

Now the company can use this to calculate CLV for an year however, this is a naive approach and works only if the preferences of the customer are same for the same period of time. So let’s explore other approaches.

Method 2

This method requires to first calculate KPI’s like retention rate and discount rate.

 

CLV = Gross margin per lifespan ( Retention rate per month / 1 + Discount rate – Retention rate per month)

Where

Retention rate = Customer at the end of the month – Customer during the month / Customer at the beginning of the month ) * 100

Method 3

This method will allow us to look at other metrics also and can be calculated in following steps:

  1. Calculate average number of transactions per month (T)
  2. Calculate average order value (OV)
  3. Calculate average gross margin (GM)
  4. Calculate customer lifespan in months (ALS)

After calculating these metrics CLV can be calculated as:

 

CLV = T*OV*GM*ALS / No. of Clients for the period

where

Transactions (T) = Total transactions / Period

Average order value (OV) = Total revenue / Total orders

Gross margin (GM) = (Total revenue – Cost of sales/ Total revenue) * 100 [but how you calculate cost of sales is debatable]

Customer lifespan in months (ALS) = 1 / Churn Rate %

 

CLV can be calculated using any of the above mentioned methods depending upon how robust your company wants the analysis to be. Some companies are also using Machine learning models to predict CLV, maybe not directly but they use ML models to predict customer churn rate, retention rate and other marketing KPI’s. Some companies take advantage of all the methods by taking an average at the end.

Kaylae Matthews

5 Applications for Location-Based Data in 2020

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Location-based data enables giving people relevant information based on where they are at any given moment. Here are five location data applications to look for in 2020 and beyond. 

1. Increasing Sales and Reducing Frustration

One 2019 report indicated that 89% of the marketers who used geo data saw increased sales within their customer bases. Sometimes, the ideal way to boost sales is to convert what would be a frustration into something positive. 

A French campaign associated with the Actimel yogurt brand achieved this by sending targeted, encouraging messages to drivers who used the Waze navigation app and appeared to have made a wrong turn or got caught in traffic. 

For example, a driver might get a message that said, “Instead of getting mad and honking your horn, pump up the jams! #StayStrong.” The three-month campaign saw a 140% increase in ad recall. 

More recently, home furnishing brand IKEA launched a campaign in Dubai where people can get free stuff for making a long trip to a store. The freebies get more valuable as a person’s commute time increases. The catch is that participants have to activate location settings on their phones and enable Google Maps. Driving five minutes to a store got a person a free veggie hot dog, and they’d get a complimentary table for traveling 49 minutes. 

2. Offering Tailored Ad Targeting in Medical Offices

Pharmaceutical companies are starting to rely on companies that send targeted ads to patients connected to the Wi-Fi in doctors’ offices. One such provider is Semcasting. A recent effort involved sending ads to cardiology offices for a type of drug that lowers cholesterol levels in the blood. 

The company has taken a similar approach for an over-the-counter pediatric drug and a medication to relieve migraine headaches, among others. Such initiatives cause a 10% boost in the halo effect, plus a 1.5% uptick in sales. The first perk relates to the favoritism that people feel towards other products a company makes once they like one of them.

However, location data applications related to health care arguably require special attention regarding privacy. Patients may feel uneasy if they believe that companies are watching them and know they need a particular kind of medical treatment. 

3. Facilitating the Deployment of the 5G Network

The 5G network is coming soon, and network operators are working hard to roll it out. Statistics indicate that the 5G infrastructure investment will total $275 billion over seven years. Geodata can help network brands decide where to deploy 5G connectivity first.

Moreover, once a company offers 5G in an area, marketing teams can use location data to determine which neighborhoods to target when contacting potential customers. Most companies that currently have 5G within their product lineups have carefully chosen which areas are at the top of the list to receive 5G, and that practice will continue throughout 2020. 

It’s easy to envision a scenario whereby people can send error reports to 5G providers by using location data. For example, a company could say that having location data collection enabled on a 5G-powered smartphone allows a technician to determine if there’s a persistent problem with coverage.

Since the 5G network is still, it’s impossible to predict all the ways that a telecommunications operator might use location data to make their installations maximally profitable. However, the potential is there for forward-thinking brands to seize. 

4. Helping People Know About the Events in Their Areas

SoundHound, Inc. and Wcities recently announced a partnership that will rely on location-based data to keep people in the loop about upcoming local events. People can use a conversational intelligence platform that has information about more than 20,000 cities around the world. 

Users also don’t need to mention their locations in voice queries. They could say, for example, “Which bands are playing downtown tonight?” or “Can you give me some events happening on the east side tomorrow?” They can also ask something associated with a longer timespan, such as “Are there any wine festivals happening this month?”

People can say follow-up commands, too. They might ask what the weather forecast is after hearing about an outdoor event they want to attend. The system also supports booking an Uber, letting people get to the happening without hassles. 

5. Using Location-Based Data for Matchmaking

In honor of Valentine’s Day 2020, students from more than two dozen U.S colleges signed up for a matchmaking opportunity. It, at least in part, uses their location data to work. 

Participants answer school-specific questions, and their responses help them find a friend or something more. The platform uses algorithms to connect people with like-minded individuals. 

However, the company that provides the service can also give a breakdown of which residence halls have the most people taking part, or whether people generally live off-campus. This example is not the first time a university used location data by any means, but it’s different from the usual approach. 

Location Data Applications Abound

These five examples show there are no limits to how a company might use location data. However, they must do so with care, protecting user privacy while maintaining a high level of data quality. 

Tehreem Naeem

Integrate Unstructured Data into Your Enterprise to Drive Actionable Insights

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In an ideal world, all enterprise data is structured – classified neatly into columns, rows, and tables, easily integrated and shared across the organization.

The reality is far from it! Datamation estimates that unstructured data accounts for more than 80% of enterprise data, and it is growing at a rate of 55 – 65 percent annually. This includes information stored in images, emails, spreadsheets, etc., that cannot fit into databases.

Therefore, it becomes imperative for a data-driven organization to leverage their non-traditional information assets to derive business value. We have outlined a simple 3-step process that can help organizations integrate unstructured sources into their data eco-system:

1. Determine the Challenge

The primary step is narrowing down the challenges you want to solve through the unstructured data flowing in and out of your organization. Financial organizations, for instance, use call reports, sales notes, or other text documents to get real-time insights from the data and make decisions based on the trends. Marketers make use of social media data to evaluate their customers’ needs and shape their marketing strategy.

Figuring out which process your organization is trying to optimize through unstructured data can help you reach your goal faster.

2. Map Out the Unstructured Data Sources Within the Enterprise

An actionable plan starts with identifying the range of data sources that are essential to creating a truly integrated environment. This enables organizations to align the sources with business objectives and streamline their data initiatives.

Deciding which data should be extracted, analyzed, and stored should be a primary concern in this regard. Even if you can ingest data from any source, it doesn’t mean that you should.

Collecting a large volume of unstructured data is not enough to generate insights. It needs to be properly organized and validated for quality before integration. Full, incremental, online, and offline extraction methods are generally used to mine valuable information from unstructured data sources.

3. Transform Unstructured Assets into Decision-Ready Insights

Now that you have all the puzzle pieces, the next step is to create a complete picture. This may require making changes in your organization’s infrastructure to derive meaning from your unstructured assets and get a 360-degree business view.

IDC recommends creating a company culture that promotes the collection, use, and sharing of both unstructured and structured business assets. Therefore, finding an enterprise-grade integration solution that offers enhanced connectivity to a range of data sources, ideally structured, unstructured, and semi-structured, can help organizations generate the most value out of their data assets.

Automation is another feature that can help speed up integration processes, minimize error probability, and generate time-and-cost savings. Features like job scheduling, auto-mapping, and workflow automation can optimize the process of extracting information from XML, JSON, Excel or audio files, and storing it into a relational database or generating insights.

The push to become a data-forward organization has enterprises re-evaluating the way to leverage unstructured data assets for decision-making. With an actionable plan in place to integrate these sources with the rest of the data, organizations can take advantage of the opportunities offered by analytics and stand out from the competition.

Aakash Chugh

Introduction to Recommendation Engines

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This is the second article of article series Getting started with the top eCommerce use cases. If you are interested in reading the first article you can find it here.

What are Recommendation Engines?

Recommendation engines are the automated systems which helps select out similar things whenever a user selects something online. Be it Netflix, Amazon, Spotify, Facebook or YouTube etc. All of these companies are now using some sort of recommendation engine to improve their user experience. A recommendation engine not only helps to predict if a user prefers an item or not but also helps to increase sales, ,helps to understand customer behavior, increase number of registered users and helps a user to do better time management. For instance Netflix will suggest what movie you would want to watch or Amazon will suggest what kind of other products you might want to buy. All the mentioned platforms operates using the same basic algorithm in the background and in this article we are going to discuss the idea behind it.

What are the techniques?

There are two fundamental algorithms that comes into play when there’s a need to generate recommendations. In next section these techniques are discussed in detail.

Content-Based Filtering

The idea behind content based filtering is to analyse a set of features which will provide a similarity between items themselves i.e. between two movies, two products or two songs etc. These set of features once compared gives a similarity score at the end which can be used as a reference for the recommendations.

There are several steps involved to get to this similarity score and the first step is to construct a profile for each item by representing some of the important features of that item. In other terms, this steps requires to define a set of characteristics that are discovered easily. For instance, consider that there’s an article which a user has already read and once you know that this user likes this article you may want to show him recommendations of similar articles. Now, using content based filtering technique you could find the similar articles. The easiest way to do that is to set some features for this article like publisher, genre, author etc. Based on these features similar articles can be recommended to the user (as illustrated in Figure 1). There are three main similarity measures one could use to find the similar articles mentioned below.

 

Figure 1: Content-Based Filtering

 

 

Minkowski distance

Minkowski distance between two variables can be calculated as:

(x,y)= (\sum_{i=1}^{n}{|X_{i} - Y_{i}|^{p}})^{1/p}

 

Cosine Similarity

Cosine similarity between two variables can be calculated as :

\mbox{Cosine Similarity} = \frac{\sum_{i=1}^{n}{x_{i} y_{i}}} {\sqrt{\sum_{i=1}^{n}{x_{i}^{2}}} \sqrt{\sum_{i=1}^{n}{y_{i}^{2}}}} \

 

Jaccard Similarity

 

J(X,Y) = |X ∩ Y| / |X ∪ Y|

 

These measures can be used to create a matrix which will give you the similarity between each movie and then a function can be defined to return the top 10 similar articles.

 

Collaborative filtering

This filtering method focuses on finding how similar two users or two products are by analyzing user behavior or preferences rather than focusing on the content of the items. For instance consider that there are three users A,B and C.  We want to recommend some movies to user A, our first approach would be to find similar users and compare which movies user A has not yet watched and recommend those movies to user A.  This approach where we try to find similar users is called as User-User Collaborative Filtering.  

The other approach that could be used here is when you try to find similar movies based on the ratings given by others, this type is called as Item-Item Collaborative Filtering. The research shows that item-item collaborative filtering works better than user-user collaborative filtering as user behavior is really dynamic and changes over time. Also, there are a lot more users and increasing everyday but on the other side item characteristics remains the same. To calculate the similarities we can use Cosine distance.

 

Figure 2: Collaborative Filtering

 

Recently some companies have started to take advantage of both content based and collaborative filtering techniques to make a hybrid recommendation engine. The results from both models are combined into one hybrid model which provides more accurate recommendations. Five steps are involved to make a recommendation engine work which are collection of data, storing of data, analyzing the data, filtering the data and providing recommendations. There are a lot of attributes that are involved in order to collect user data including browsing history, page views, search logs, order history, marketing channel touch points etc. which requires a strong data architecture.  The collection of data is pretty straightforward but it can be overwhelming to analyze this amount of data. Storing this data could get tricky on the other hand as you need a scalable database for this kind of data. With the rise of graph databases this area is also improving for many use cases including recommendation engines. Graph databases like Neo4j can also help to analyze and find similar users and relationship among them. Analyzing the data can be carried in different ways, depending on how strong and scalable your architecture you can run real time, batch or near real time analysis. The fourth step involves the filtering of the data and here you can use any of the above mentioned approach to find similarities to finally provide the recommendations.

Having a good recommendation engine can be time consuming initially but it is definitely beneficial in the longer run. It not only helps to generate revenue but also helps to to improve your product catalog and customer service.

Signavio

Six ways process mining in 2020 can save your business transformation

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The lack of information about existing processes kills 70% of large transformation projects and around 50% of RPA projects…alarming statistics. Triggering this failure rate is a lack of understanding about existing processes, and the disconnect between the discovery, visualization, analysis, and execution of existing data. So, banish the process guesswork! Utilizing process mining technology unlocks the information, visibility, and quantifiable numbers needed to improve end-to-end processes for sustainable transformation.

Read this article in German:

Wie Process Mining 2020 Ihre erfolgreiche Geschäftstransformation 2020 sicherstellt

 

Process mining in 2020

Your data fingerprint

If we consider the figures again (from McKinsey and Ernst & Young (EY) respectively), the digitization of products and services is forcing companies of all shapes and sizes, and in all industries, to dramatically reconsider their existing business models and the processes they implement. Because all activities are different, process mining uses the unique data—your company’s business fingerprint—to automatically piece together a digital representation of all your existing business processes.

This digital evidence enables us to visualize exactly how processes are operating (both the conventional path and variable executions) down to individual process instances. In other words, you can unearth processes which lie unseen or dormant, revealing hidden value, and providing an instant understanding of complex processes in minutes rather than days.

Triggering dormant success

Then, with standardized and configurable notifications and KPIs, you can further understand the immediate impact of any process change made—meaning that failure rates decrease, and company confidence is improved. And that’s not all: everyone from new employees to the C-suite can better visualize, understand, and explain their organization’s processes. This ensures that the right process change is secure and that improvement has the intended impact, every time.

Unleash the power of process

In business, we all answer to somebody, and it is critical to connect problems to real solutions. The everyday functions of companies—the processes upon which they are built—are the connection to business tech, from “process” mining to robotic “process” automation. Without process understanding, the tech is redundant because we have no idea how work has flowed in an existing application. Process is the lifeblood of operations.

Process mining: your point of differentiation

Transformative digital technology integration

In addition to the DVA of process mining—discover, visualize, analyze—is the power to monitor real-time process execution automatically from your existing data. This simple point and click assessment can provide an instant understanding of complex processes. Within transformation projects, which by their very nature require the profound transformation of business and organizational activities, processes, competencies, and models, process mining provides the visual map to facilitate immediate action.

This self-sustaining approach across an entire organization is what leads to genuinely sustainable outcomes, and builds a process culture within an organization. By taking this holistic approach, digital transformation and excellence professionals will find it easier to leverage processes, justify their projects and programs, and address behavioral change challenges.

This includes the facilitation of transformative digital technology integration, operational agility and flexibility, leadership and culture, and workforce enablement.

Three ways process mining can save your business in a transformation project:

  • You require 100% operational transparency: To chart all your transactions requires complete process transparency. This capability allows the direct comparison of actual operations to the ways that processes were designed to occur. This conformance checking can automatically identify the highest priority issues and tasks, and highlight root causes, so we can take immediate action.
  • You must reduce costs and increase efficiency: Signavio research shows that almost 60% of companies incurred additional charges from suppliers due to process inefficiencies. Process mining can help your business reduce costs because it finds vulnerabilities and deviations, whilst highlighting what is slowing you down, including the bottlenecks and inefficiencies hampering revenue. Process mining beefs up operational health via process improvements and pre-emptive strategies.
  • You must optimize the buying and selling cycle: Is shipping taking too long? Which of your suppliers supports you least? Who is outperforming whom? Process mining is your one-click trick to finding these answers and identifying which units are performing best and which are wasting time and money.

Process mining and robotic process automation (RPA)

The beneficial fusion of both technologies

Robotic process automation (RPA) provides a virtual workforce to automatize manual, repetitive, and error-prone tasks. However, successful process automation requires exact knowledge about the intended (and potential) benefits, effective training of the robots, and continuous monitoring of their performance. With this, process mining supports organizations throughout the lifecycle of RPA initiatives by monitoring and benchmarking robots to ensure sustainable benefits.

Upgrade robot-led automation

These insights are especially valuable for process miners and managers with a particular interest in process automation. To further upgrade the impact of robot-led automation, there is also a need for a solid understanding of legacy systems, and an overview of automation opportunities. Process mining tools provide critical insights throughout the entire RPA journey, from defining the strategy to continuous improvement and innovation.

 Three ways process mining can save your business in an RPA lifecycle project

  • You require process overviews, based on specific criteria: To provide a complete overview of end-to-end processes, involves the identification of high ROI processes suitable for RPA implementation. This, in turn, helps determine the best-case process flow/path, enabling you to spot redundant processes, which you may not be aware of, before automating.
  • You are unsure how best to optimize human-digital worker cycles: By mining the optimal process flow/path, we can better discover inefficient human-robot hand-off, providing quantifiable data on the financial impact of any digital worker or process. This way, we can compare human vs. digital labor in terms of accuracy, efficiency, cost, and project duration.
  • You need to understand better how RPA supports legacy processes and systems: RPA enables enterprises to keep legacy systems by making integration with cloud and web/app-based services, transforming abilities to connect legacy with modern tools, applications, and even mobile apps. Efficiency and effectiveness will be improved across crucial departments, including HR, finance, and legal.

Process mining for improved customer experience and mapping

Reconfigure customer delight

The integration of process mining with other technologies is also essential in growing the process excellence and management market. With process management, we already talk about customer engagement, which empowers companies to shift away from lopsided efficiency goals, which often frustrate customers, towards all-inclusive effectiveness goals, built around delighting customers at the lowest organizational cost possible.

Further, the application of process mining within customer journey mapping (CJM)—especially when linked to the underlying processes—offers the bundled capability of better business understanding and outside-in customer perspective, connected to the processes that deliver them. So, by connecting process mining with a customer-centric view across producing, marketing, selling, and providing products and services, customer delight becomes a strategic catalyst for success.

Unlock the full potential of process

Trigger process mining initiatives with Signavio Process Intelligence, and see how it can help your organization uncover the hidden value of process, generate fresh ideas, and save time and money. Discover more in our white paper, Managing Successful Process Mining Initiatives with Signavio Process Intelligence.