Archive for Rebecca Camus

The Importance of Real-time Prescriptive Solutions in the Customer Lifecycle Management in the Airline Industry

 

In the last few weeks, the news and the internet have been filled with stories about the lack of customer service in service industries, especially the airline industry. Many companies in the service industry often forget that they’re in the customer business and take their patrons for granted. No incentives are offered, no customer engagements are made and no “surprise and delights” are given to reward customers for their loyalty.  What so many companies don’t realize is that if positive interactions with their customers aren’t made, it is too easy for customers to leave for competitors or tweet negative feedback for the world to see. This is especially relevant in industries, like domestic airlines, where there are many competitors that offer similar services. If customers are dissatisfied with a certain airline, many times there are multiple alternative airlines that offer similar routes. Because of the prevalence of competitors and easy access to the internet to search for other options, service oriented businesses need to be extremely conscientious about their customer lifecycle management and ensure that customers are having positive interactions with them through all steps of the lifecycle. If companies do not, they put themselves at risk for losing customers.

So, what is customer lifecycle management? The customer lifecycle consists of the acquisition, conversion, retention and loyalty of customers. This lifecycle needs to be closely monitored by marketers – however, this can be challenging because to effectively do this, a marketer needs to ensure that all relevant data is being collected about each potential and current customer. This data then needs to be interpreted and immediate personalized engagement needs to be made. This is particularly challenging because with more customers shopping online or using mobile apps, huge amounts of data are being generated every minute. Prescriptive solutions need to be in place to not only collect this information, but also to interpret that data, to identify what information is relevant and then automatically use this information to engage with the customer.

Real-time prescriptive solutions take data from multiple sources and then in a “non-scripted” way, interact with a customer as they’re in a store or experiencing a service. So for example, let’s say a platinum status airline customer is on a flight that gets delayed. This passenger has been on 3 other flights in the past 2 months that have gotten delayed. The solution would look at the historical and current information and proactively schedule the customer on the next flight. Depending on what the customer has set in his preferences, the customer would also be rewarded with a $100 voucher, an upgrade for himself and his traveling companions or additional mileage added to his frequent flyer account. A similar customer who may have had no other delayed flights in the last 2 months would be presented with offers that are less in value like a $25 voucher. A customer with no status and 2 previous negative interactions may only receive a free drink voucher.

Prescriptive analytics can also look at the overall patterns that a customer has. For example, a customer prefers window seats and always orders tomato juice on morning flights. However, when returning home this same customer will order an alcoholic drink and is likely to upgrade to business class. If a solution is implemented, flight staff can be alerted to this and “surprise” this guest on a night flight with a free drink. An alert can be pushed to this customer before his flight to purchase an upgrade at a discounted price, thus ensuring that he gets the business class seat that he wants. These proactive engagements will not only make the customer happy but also allow the airline to upsell the customer and ensure that a positive interaction has occurred.

These examples were just for current customers, however, airlines and other service industries can use real-time prescriptive solutions throughout each step of the customer life cycle. These positive interactions will be critical to the success of airlines and other companies in the service industries in the foreseeable future. For more information on how customers can be engaged through each step of the customer life cycle or our real-time prescriptive analytics solutions, please e-mail me at rebecca.camus@entrigna.com

 

 

Oh the Places You'll Go.....with IoT

Last week we attended the IoT NA conference in Rosemont, IL. As I stood at the booth I was amazed at not only the number of regional companies that were attending but also the different industries that were represented. I spoke to people from traditional industries like automotive and tech manufacturing, but I also spoke with attendees from a children’s museum, a drone company and several small cities across Illinois. Conversations with these people from “non-traditional” sector really got me thinking about how IoT can be utilized in pretty much every sector and the first movers from non-traditional industries will not only be able to give themselves an unbelievable competitive edge but also provide an incredible experience for their customers.

When the attendees from the museum came by, I could tell that they were on a scouting mission. They definitely recognized the value of using data from IoT, but were unsure of where to start. It makes sense --- when looking through the agenda of this conference there were fantastic sessions on sensors, the power of analytics and even monetizing IoT data. However, most sessions were aimed at manufacturing, because of course, manufacturing with its preventive maintenance and process improvements is the current leader in IoT projects. However, it may have seemed like a one-sided conference, however, many of the principles discussed can be applied to non-traditional industries. For example, many museums already have apps. Museums could use geolocation on the user’s phone to track how people are walking through the museum. This information, just like data collected from forklifts driven around a distribution center can be used to see who is traveling where, where people are stopping and how many people are visiting exhibits during certain time periods. This information can be invaluable to a museum (or a store or an airport and the list goes on….). It can show designers if current pathways are intuitive, identify which parts of an exhibit are the most/least popular and create better pathways for guests. If this information is utilized, exhibits can be tailored to what patrons are really interested in and also make sure patrons can easily navigate through exhibits – thus a happier guest who is more likely to return. Additionally, museums could combine information from a customer’s past navigation history and purchase history with their current location in the museum or even time that they’re in the museum. In real-time the app could make recommendations to the patron for special events occurring that day (for example, a lego building session for a family that has previously purchased legos or walked through a lego exhibit in the past) or current sales in the gift store when they are getting ready to exit the museum.

At the end of the day, non-traditional industries will not have out of the box solutions targeted to their specific needs. However, with research on what other industries are doing and a little creative thinking non-traditional industries can make very powerful and differentiating solutions. To take a page from Dr Suess these non-traditional industries needs to think about all of the places they can go…..with IoT.

For more information on how to get started with an Internet of Things project, please visit our website or e-mail us at info@entrigna.com.

IoT in Transportation and Logistics

Recently, the idea of data and artificial intelligence has been making its way to the forefront of the news, especially in the form of driverless cars. However as frighten as this seems to many people, big data and the Internet of Things can play a very large and beneficial role in improving transportation and logistics without getting too Sci-Fiy for the average American.

So how can big data and the IoT help this industry? Well, obvious answers involve preventive maintenance. Don’t get me wrong, preventative maintenance is a great thing. By predicting when a part is going to break you can proactively replace it or service the machinery whether it’s an 18-wheeler or a piece of machinery. By having preventative maintenance as part of your business strategy, you’ll ultimately have more uptime which leads to higher revenue. However, this is so 2016. Preventative maintenance is just the tip of the iceberg when it comes to using the IoT in Transportation and Logistics.

One other idea on how companies can use IoT to streamline their processes involves prioritizing emergency calls. Many companies rely on dispatchers to get their service technicians to a call as soon as possible. However, when many service companies get a call to respond to an emergency, the dispatcher sees who is free in the area and then picks a person to respond to the call. Typically, there is not an automated process not only to identify what technicians are in the area but also how certain conditions such as weather, traffic or construction could possibly effect how long it would take the technician to respond to a call. In theory, a technician a mile away from a call could take much longer time to respond if a road is closed rather than another technical who may be ten miles away but coming from the opposite direction that doesn’t have traffic. The IoT can help solve this challenge. By combining current weather, traffic and construction conditions with the location of the technician, companies can automatically identify which technician or responder would make it to the call in the shortest amount of time.

The power of the IoT can also be seen by the use of RFIDs. RFIDs are tiny sensors that many parts and products are labeled with. Many companies use RFID technology to reactively manage inventory. So, when a company “does inventory” employees are told how many items are on a shelf instead of manually scanning each item on the floor and in the stock room (that was always my least favorite part of my college job at Banana Republic). However, the real power in these little chips is the ability to proactively manage your inventory. By using RFIDs, employees can be alerted when inventory levels are running low. Items can either be restocked from inventory or re-ordered. For more cool ways to see how RFIDs can be used in a logistics setting, check out our video on how the IoT is Revolutionizing Manufacturing.

For more information on how to get started with an Internet of Things project, please visit our website or e-mail us at info@entrigna.com.

 

 

Big Data in Agriculture

Over the next few months, I'll be writing blogs on some non-traditional industries that use big data. I'm looking forward to sharing updates and information on how big data can be used in all industries, not just the ones we typically associate with technology.

Farming is something most of us take for granted. We go to the grocery store and pick out our food without giving much thought to where our food came from or what went into growing it. We think of small quaint farms where farmers plant seeds, ride small tractors and then harvest their crops. However, many farms in the United States rely heavily on technology and are turning to big data to help them become more efficient, cost-effective, and less environmentally impactful.

Today’s tractors not only use sensors to collect information that help with preventative maintenance, but tractors also have multiple computer screens and sensors on them that collect information from everything from nitrogen and pH levels in the soil to how far apart the seeds are. Farmers tend to use this information while planting, however, many farmers do not use the information they’ve collected after the fact.

Farmers are also using “precision farming” to help make farming more efficient. This technique can mean many things, but ultimately it means using information about the soil and crops in a specific area to maximize the output of the crop and minimize the production cost for a crop. Farmers can use this information for everything from identifying the best places to plant certain crops to how many plants per acre they can plant.

In the future, we can expect to see more farmers adopting precision farming and other big data techniques. The big data market for agriculture is expected to grow from a $2.4B industry in 2014 to a $5.04B industry in 2020 (Research and Markets Global Precision Agriculture Market 2014-2020) and with the population projected to grow to 9 billion people by 2050, farmers will need to increase outputs significantly to keep up with demand. We’re already seeing some very interesting ways that precision farming and big data solutions us can be implemented at larger facilities. For example, Gallo Winery recently implemented a system that takes satellite imagery of their vineyards and determines which plants are getting too little or too much water. The images are processed, analyzed and then the sprinkler that is connected to an individual plant is automatically adjusted to give it either more or less water.  Water consumption at Gallo Winery has been reduced by 25% since the system was implemented, the health and production of the plants has increased and the costs associated with workers manually watering individual plants has decreased.

The real power of big data will be when farmers start sharing their data with companies. In the past, farmers have been very hesitant to share the data they collect to corporations. Many farmers view the information from their fields as propriety and are worried that the information generated from their farms will be shared with commodity traders or other farmers. They are also worried that seed and equipment companies will use the information to sell farmers higher prices goods. However, seed and equipment companies need information from individual farms in order to improve their software and products so farmers can keep achieving the best results possible. In the next few years, I believe seed and equipment companies will start focusing on how to earn the trust of farmer and proactively show farmers how sharing this information will lead to substantial ROIs for the farmers. Also, as time progresses farmers will become more comfortable with big data and the technologies and realize that the payoff of higher yields and ultimately lower costs will persuade farmers to share their data.

Trends in Big Data and the IoT in 2016

As we enter the new year, it’s always an exciting time to reflect upon the previous year and ask “What new things will happen next year?” Over the past year, it’s been really cool to see how executives at companies are realizing the value of using big data instead of just collecting it.  Because of this trend, 2016 should bring about disruptive changes in the big data and internet of things markets.

Some of the top trends in 2016 that I see happening are

Customer satisfaction levels will be influenced by an automatic personalized experience

As consumers become more tech savvy and more millennials have discretionary income, more consumers will continue to adopt and use mobile apps such as Target’s Cartwheel or PriceGrabber while they’re shopping. These consumers are looking for a personalized experience that will give them some benefit, whether it’s a lower price or targeted advertising or coupons based on past behaviors, when shopping. Consumers have many options to choose from when shopping both online and in-person and will ultimately pick the store that gives them the most value and the best shopping experience.

Additionally, with the increase of internet shopping and the multitude of stores available to consumers, companies will start relying more on what an individual is clicking on and posting on-line about products and her shopping experience. In the past, companies have had challenges making sense of this information in a timely manner and then reacting. However, companies are starting to discover solutions that can help them not only react in real-time to a customer’s shopping experience but also personalize the customer’s shopping experience based on past behaviors or trends. These proactive actions should lead to higher level of customer satisfaction for the customer.

Using ROI in big data

Executives are pushing for the adoption of big data solutions however, many executives want to see a measureable ROI and meaningful use cases before they make a large investment in a solution. In 2016, solution providers will start partnering with their users to determine the ROI of using a solution. Many times these measurements can be straightforward, such as calculating how much revenue is saved when using data sensors to predict when parts will wear out.  However, calculating the ROI on other solutions that combine structured and unstructured data will be more challenging to determine.

Data in the Internet of Things will start to be used instead of just collected

Sensors on many devices will help companies predict when parts need to be serviced and can also predict anomalies in the overall system. However, many companies have yet to realize the full potential of this data. In 2016, more companies who collect this type of information will no longer just store it but start to use this information to help prevent down time and achieve better customer service. Also, with the increased adoption of personal healthcare devices, such as Fitbits and smart watches, more consumers are going to start tracking their own healthcare.  Companies that provide solutions that monitor and make recommendations on a consumer’s heart rate, blood pressure or fitness activity will grow.

The need for simplified Big Data

Currently, many of the traditional big data solutions that make real-time decisions require users to be very tech-savvy and require substantial coding. However, in 2016, we will probably see more companies purchasing tools that can be easily used by non-technical users. This is because there is currently a shortage of data scientists and the average salary of an entry level data scientist is quite high compared to that of an entry level analyst. Many companies just can’t afford to have data scientists on staff.  Also, customer facing groups want to be able to see results in real-time and not wait for the IT or data science group to get them the information they need. Solutions will still need to be set up by data scientists and software engineers, however, once the solution is set up, non-technical groups such as marketing and customer service will be the ones accessing the data and writing simple queries to find the information that they need in real-time.

2016 will definitely be an exciting time for big data! The Entrigna team is looking forward to working with companies in the next year to discover how we can help them make and achieve their big data goals! For more information on Entrigna please e-mail info@entrigna.com.