The consumer packaged goods market is an especially competitive one. The global nature of the market and the presence of so many major players mean it's vital to regularly seek out new opportunities and avenues for improving everything from product development and manufacturing to supply chain and customer engagement concerns. Predictive analytics can play a major, positive role in increasing operational efficiency, identifying potential problems and highlighting areas for growth and development. As just one example, CPG companies can tap into existing and new streams of customer data to better understand the people that buy their products.
"Understanding the needs of consumers is central to continued success for CPG manufacturers."
Developing better customer intelligence through predictive analytics
Understanding the needs of consumers is central to continued success for CPG manufacturers. With business intelligence software solutions like the IBM Cognos suite of products, organizations can dramatically improve their ability to gain insight into the preferences and behavior of shoppers. Having that edge, no matter how it's specifically applied to a given product, line of merchandise or the business as a whole, means giving customers items that better match their wants and needs. In combination with effective marketing – which can also benefit from predictive analytics – such an approach can easily lead to improvements in sales and revenue.
Progressive Grocer pointed out the value of social media and digital interactions with consumers in terms of garnering more information to analyze. These channels offer businesses new dimensions of customer data, allowing them to form more detailed and complete pictures of those shoppers and optimize product development, marketing and engagement efforts. At its heart, more information on hand means more confidence in decision-making, a pervasive benefit that creates a positive impact across operations. A deeper, more holistic understanding of customers that continues to develop and change as new information is gathered means consistency and an advantage CPG manufacturers can continue to rely on for many years to come.
Aviana understands the many unique elements that set the CPG industry and its constituents apart from other segments of the economy. We know how central the relationship between customers and manufacturers is to continued success, and our big data and predictive analytics solutions tap into valuable data related to that connection. Our solutions can benefit internal operations like financials and inventory management, too, which means your company can apply the advantages of predictive analytics wherever they're needed or desired. To learn more, visit our dedicated industry page.
There are plenty of issues consumer packaged goods manufacturers have to contend with, from the general concerns faced by every business to more industry-specific considerations. One of the most important for CPG organizations is effective use of data to drive sales and customer engagement in a sustainable and adaptable way. Big data can be the key to success in that respect. Big data analysis draws on the high volume of data created by national- and global-scale CPG operations and sales to create increasingly attractive products, better customer experiences and more powerful marketing campaigns.
Utilizing big data in the most effective ways possible
In some ways, the CPG industry has a leg up over others in terms of understanding consumers. The large number of transactions and the global or international nature of many companies means there's plenty of data to identify, collect and analyze to further business objectives.
The IBM Big Data & Analytics Hub pointed to the traditional combination of informational sources used by the CPG industry in the past to identify marketing, sales and customer engagement opportunities. Organizations used internal and external data streams to develop new products, tweak existing ones and find ways to effectively reach out to shoppers. While those sources of information are still valuable and important, they're no longer the only major forms of data that are important – and in some cases vital – to continued growth and development in the CPG market.
The unstructured data that flows from channels like online reviews, social media interactions, allows for more targeted marketing and product development. For example, IBM pointed to a new strategy developed by the Kraft Foods Group. After recognizing the declining value of traditional advertising, the company started effectively targeting specific demographic groups and sharing context-dependent marketing and engagement messages. Such an approach allows for more relevant outreach, which in turn creates stronger and more positive relationships than encourage future purchasing.
Embracing big data and predictive analytics is a critical step forward for CPG manufacturers, and Aviana knows how to work with companies in the industry to achieve the best results. Choosing the right business intelligence software and data warehousing solutions and implementing them in a way that makes sense for individual companies is of the utmost importance. Aviana helps CPG businesses not only set up big data and predictive analytics solutions, but make them as effective as possible. To learn more, visit our dedicated industry page.
Manufacturers still fulfill the same roles they did during the industrial revolution, but the processes, systems and tools used to reach that end goal consistently and drastically change. From the development of efficient electrical power to the continuing digitization of workflows, there are always improvements for manufacturers to consider and implement. Using predictive analytics to increase the effectiveness of maintenance efforts and prevent common causes of delays and reduced production is a currently developing trend and one that may prove especially beneficial for manufacturers.
"Nearly all aspects of operation can benefit from data collection, forecasting and proactive decision-making."
Predictive modeling tools increase insight, opportunities for proactive maintenance
Avoiding issues that require machines to shut down and force workers to stand idle leads to simple but powerful benefits: more time spent actively producing materials or products and fulfilling orders quickly, with a higher degree of confidence. The IBM Big Data & Analytics Hub discussed the application of predictive analytics in one especially important area of manufacturing, the supply chain. By using sensors to provide information about the operations of trucks, ships and planes, businesses can consistently gather a variety of details about the status of the vehicles used in their supply chain efforts.
By also harnessing the power of predictive analytics, manufacturers can take the next step and not only use the information to make decisions in the present, but also make valuable predictions about future functions and potential problems. This proactive approach adds significant value to the investment into data-gathering efforts, allowing companies to engage in preventative maintenance and resolve problems before they occur. In terms of the supply chain alone, such an effort cuts down on delays and missed deadlines, provides a better experience for customers and reduces the possibility of incoming supplies not reaching facilities in a timely manner.
Of course, predictive analytics applies to much more than just the supply chain – nearly all aspects of operation can benefit in some way from data collection, forecasting and proactive decision-making. Aviana offers robust experience working with the manufacturing industry and a variety of industry-leading predictive analytics and big data solutions from IBM that meet the needs of a wide range of manufacturers. To learn more about how predictive analytics can benefit your organization, visit our dedicated manufacturing industry page. It includes specialized information, case studies and much more.
Seasonal products are a major component of many manufacturers' output. While the concept may bring Halloween costumes and Christmas decorations to mind, there are many products that are only produced or sold during certain times of the year and have nothing to do with holidays. For large manufacturers that operate in many different countries and regions, the likelihood of seasonal production increases. To accurately forecast demand and plan ahead – needs made more pressing by the lack of continuous data about cost, sales and many other considerations – predictive analytics is an essential consideration.
"Using business intelligence software and similar tools is no longer optional."
Predictive analytics becoming a necessity in the manufacturing world
According to statistics shared by Forbes from a Honeywell survey, 68 percent of manufacturers are currently investing in data analytics and close to half of all such companies surveyed believe using business intelligence software and similar tools is no longer optional. The most telling statistic gathered during the survey may be the indication of how many manufacturers see analytics as vital, above and beyond most other needs. About 67 percent of manufacturers will invest in analytics even though market conditions and operational considerations mean reducing spending in many other areas.
The general value of predictive analytics is well-known, and the ability to accurately forecast future demand on a seasonal basis is even more vital. With a host of unique, time-dependent considerations to make and less data to use in that process – along with all of the considerations that come with regular manufacturing – effective seasonal production is a difficult proposition. However, the right analytics solutions and effective approach to managing these concerns can lead to very positive results.
Aviana's work with Kawasaki Motors Corp. was focused on seasonality and conquering the related complexities of production. Many of Kawasaki's high-performance sports vehicles are made on a seasonal basis and the company needed an increased level of insight to enhance planning efforts and improve its performance. By working with Aviana to build on its existing IBM Cognos TM1 application and reinvigorating its IBM Cognos Business Intelligence environment, Kawasaki improved reporting outcomes and allowed more than 200 users to gain earlier access to more accurate information. To learn more about Aviana's work with businesses in the manufacturing and heavy industry sectors, visit our dedicated resource page.
With so many elements of the consumer packaged goods field deeply tied to the quickly changing preferences of consumers, it's no surprise big data and predictive analytics can provide major advantages to the industry. Working with the right partner to select the most effective IBM software and implement it in an efficient manner can mean a world of new insight is suddenly available for a wide range of businesses in this market. It's important that companies moving to or already utilizing these software solutions recognize the many ways big data and analytics can help, and don't get too caught up in just a few of the potential advantages.
"The increased availability of many different dimensions of data is a major industry shift."
Many different applications of big data provide a holistic benefit to CPGorganizations
One major shift realized in the industry in recent years is the increased availability of many different dimensions of data, frequently including information that was difficult or impossible to obtain in the past. Social media has significantly lowered the barriers to collecting customer insight and, as IBM Big Data and Analytics Hub writer Barbara Thau pointed out, plenty of unstructured data flows from the digital footprints of consumers in areas beyond social media. Organizing, cleansing and standardizing this data is an important consideration, as is understanding all the different ways in which it can be applied to product development, marketing, promotions, distribution and many other elements of operation.
One example of a major CPG manufacturer thinking outside the box with help from big data is Unilever, according to Thau. Armed with relevant data about potential customers, market reach and other considerations, the company decided to avoid traditional marketing demographics with a new hair-care product assortment. Instead, the business focused on different hair types and the specific needs that come along with them in its promotional efforts. In a separate IBM Hub article, author Randall Howard considered the more general example of effective promotions. With only a minority of CPG promotions proving effective, big data has the power to fill the gaps, highlight potential issues and steer businesses toward more positive results.
With so many different areas of operation ready to be changed by the use of big data, it's critical that CPG businesses work with the right partner to implement the best possible solution and have access to ongoing support. Aviana's work in the CPG industry offers a wealth of experience and proven results for companies that want to initiate or improve efforts to utilize big data and analytics.
In every market, connecting with customers, creating positive engagement and retaining them in the long term are critical considerations. For highly regulated industries like financial services, it can be significantly more difficult to address these concerns. Myriad rules and legislation can make it difficult to offer products and services substantially different than those provided by competitors. The edge in this field can often come from steering patrons toward best possible match for their needs, taking a number of general and specific factors into account. In many instances, educated, relevant recommendations stem from the use of predictive analytics and business intelligence software.
"Effective anticipation through predictive analytics provides a real value for customers."
Understanding and anticipating customer needs
Financial service providers with the technological capability to derive insights based on historical and current customer data and decisions have a real advantage over those that don't. The power of predictive analytics allows financial services companies to understand more about their customers, what decisions they're likely to make in the future and where their current situation may lead to. This is one major element of effective connections with patrons, as highlighted in an infographic from the IBM Big Data and Analytics Hub.
When businesses can anticipate a number of different events in the life of a customer – from the short-term need for an additional line of credit to opportunities for investments, additional accounts and other offerings – they can appear more relevant and useful. This in turn encourages positive feelings toward the financial service provider, like a sense of trust and a desire to continue the relationship – or even expand it. Effective anticipation through predictive analytics provides a real value for customers, ideally presenting them with sensible and attractive options just as soon as or slightly before the patrons themselves completely recognize the related need or desire. When clients recognize your company as one that effectively anticipates and responds to their needs, that benefit extends to the business as well.
Aviana knows how important early identification of a number of factors, from risk to upsell opportunities, are in the financial services industry. Our experience in the field helps us understand the unique needs and desires of companies filling a variety of financial services roles and assist them accordingly. Read more about our approach and past successes by visiting our industry page.
In an established industry with a long history of development and refinement, businesses have to seek out all reasonable opportunities to improve. For companies in the consumer packaged goods world, that means looking at the process of making products – from the first receipt of raw materials to the arrival of finished, packaged products at the loading docks of purchasers. Applying predictive analytics to every stage of operations and using business intelligence software to not only gather data but derive insight is a critical component of this effort, and one that allows CPG organizations to increase understanding in all of their functions.
"Applying predictive analytics to every stage of operations gives businesses a high degree of insight."
The power of a holistic view of supply chain operations
In an article for the IBM Big Data & Analytics Hub, consumer products writer Ritika Puri said the lack of automation and complete visibility in terms of information and analysis has harmed CPG businesses striving to make operational improvements. When distinct but connected parts of a process are viewed as wholly separate entities, business leaders can't see all potential areas for improvement. Developing a global view of operations is critical, as is examining the junctions between two workflows and the details of the workflows themselves. By using a solution like IBM Cognos BI, CPG manufacturers develop a more complete view of their operations and, with the power of predictive analytics, have a variety of actionable insights on hand.
Increasing automation is one area where many organizations can realize benefits in a relatively short period of time. One of the biggest benefits business intelligence software provides is making the collection of critical metrics a smooth, continuous process with very little if any room for human error. This high standard of data quality, along with the ability to gather all such information and present it consistently and simultaneously, means a holistic depiction of all current activities is possible.
With so many choices and options available to CPG businesses, finding the right predictive analytics platform and the right partner can be an especially difficult task. Aviana has actionable experience working in the CPG field and offers an array of software solutions and customizability to suit the many unique needs in this sector. To learn more about all the areas of operation we address and the power of holistic insight and analysis of operations, visit our dedicated industry page.
Efficiency is a universal concern for businesses, although the opportunities to streamline operations and make processes more effective vary greatly from industry to industry. For the hospitality sector, there are plenty of areas where predictive analytics and business intelligence software are driving new workflows and developing actionable intelligence and insight. With solutions like IBM Cognos in place, organizations in the hospitality industry are positioned to draw on many streams of data and craft improvements to internal and guest-facing concerns.
Making the most of guest experiences
Beyond fulfilling the basic needs and meeting the universal requirements of guests – like clean rooms and a staff ready to respond to potential issues – there are many different areas the hospitality industry can develop to fulfill customer expectations. Better identification and understanding of guests is one such avenue, and it ultimately benefits both businesses and customers. Forbes pointed out how effective targeting of guests, like differentiating between low- and high-value ones and developing an understanding of individual preferences, makes resource allocation more efficient and creates stronger, more positive relationships.
"One way to view the power of predictive analytics in the hospitality industry is the concept of crossroads."
One way to view the power of predictive analytics in the hospitality industry is the concept of crossroads. Intersections create options and allow for the development of different paths based on convenience, preference and many other factors. Businesses in the hospitality industry can use predictive analytics to increase the number of metaphorical intersections included in their operations, creating different paths that are optimal for the many types of visitors they encounter. A straight line from check in to check out may be more efficient for a low-value guest who has little inclination to spend outside of the essentials. On the other hand, a longer, more complicated path is necessary for a high-value customer who is shown by analysis to be more apt to visit spas, restaurants and other amenities that add value to a visit.
Aviana's continuing work in the hospitality industry means we're up to date on industry trends. We understand how new strategies and approaches can be supported and enhanced through predictive analytics. We can point to other, more traditional functions that can be improved through increased data collection and analysis as well. To learn more about what we do and review case studies of our work with hospitality leaders like MGM Resorts International, visit our dedicated industry landing page.
Big data is in a unique position in the business world. The use of many dimensions of information for analysis and subsequent action isn't just a pure advantage anymore. It's moving toward a mix of a benefit and a requirement as more companies utilize it. The question is less frequently whether or not a business uses big data at all, but how it utilizes that resource. Selecting the right business intelligence software, like IBM Cognos, helps financial service providers make the most of the many streams of data that flow in and out of operations on a daily basis. There's still work to be done and future developments to realize before big data and financial services are fully and truly merged, but the industry is inexorably moving in that direction.
Data management is a major concern
As Investopedia pointed out, drawing on information originally gathered by IBM, each day sees a massive generation of data cross the internet. Most of the world's data was created in the past few years, and approximately 2.5 quintillion bytes of data are created on daily basis. The drastically increased availability of information means the financial services industry has plenty of new and exciting ways to capitalize on customer, market and historical data and indicators, among many other resources.
Investopedia highlighted the need to take the right approach to big data to realize success, a point that grows more important as big data and predictive analytics become increasingly common in the financial services field. Concerns about data collection in some areas and what it may reveal about customers – including sensitive or legally protected information – is a major issue. So is focusing on short-term investment or trading results, as issues with signal-to-noise ratios and other factors can cause problems without the right analytical systems in place.
Aviana's work in the financial services industry means it has the experience and knowledge necessary to work through and avoid many of the most common issues related to big data. Our suite of IBM Cognos offerings allows for a range of choices, and customization and support helps individual businesses get the most out of big data and address unique concerns. To learn more, visit our dedicated industry page for financial services.
Predictive analytics provide major benefits to healthcare providers, helping staff more effectively treat patients and reduce the labor and resources needed to resolve each patient's problems. The benefits of analytics are many, but the idea of more efficient care is especially attractive because of the positive effect it has on all major areas of operation. Staff can more accurately diagnose and treat patients, which also means they can interact with more individuals during the course of their shifts.
The potential for both a higher volume of care and more accuracy in each interaction are two major factors driving healthcare providers to use business intelligence software like IBM Cognos solutions.
"One consistent issue for healthcare providers across the country is demographic and individual risk factors."
Understanding lifestyle habits and risks
One consistent issue for healthcare providers across the country is demographic and individual risk factors, like higher-than-average consumption of junk food or tobacco in certain areas. The IBM Big Data & Analytics Hub highlighted a lifestyle habits project in the Carolinas undertaken by Carolinas HealthCare System, which has approximately 900 care centers across those two states.
The IBM report drew on information originally reported by Bloomberg, which highlighted how CHS is using transactional data from credit card transactions and other sources to predict future issues and engage in preventative measures. The approach used by CHS includes both individual-level data, like purchasing cigarettes, and information about the areas in which patients live, like pollen count. That information is then used, for example, to assess the risk of a patient with asthma who aligns with both of those factors having to make a visit to the emergency room in the near future. The predictive models inform doctors about potential issues before they happen, but the system also takes patient privacy issues into account. While doctors are apprised of the results of the analytical efforts, they aren't told of the individual data points – a concession to the need for patient privacy.
Aviana offers healthcare providers interested in predictive analytics a high level of experience inside the industry. Our track record contains a number of successful projects, including efforts focused on care and administrative concerns. To learn more about our work with big data and predictive analytics in the healthcare industry, visit our dedicated, on-demand industry solution video series page. You can view videos on topics ranging from predicting admissions to risk mitigation and learn how our solutions help providers transform operations.