"The purpose of marketing is to fully understand and understand consumers, so that products and services meet the needs of consumers, and consumers will take the initiative to buy rather than sell." The famous words put forward by Mr. peter drucker, the father of modern management 40 years ago, still have guiding significance for our marketing today.
What is the value of big data?
With the advent of the multi-screen era, consumers' search, access, transaction and social behavior on the Internet have produced a huge amount of Internet information data. How to obtain these data from the outside, whether you are good at managing these data and creating products and services that meet the needs of consumers according to these data will become the key to the success of enterprises. This planning principle from the outside to the inside is one of the guiding principles of integrated marketing communication proposed by Dr. Don Schultz. According to the research results of IDC and McKinsey's big data, big data can dig out huge commercial value in the following four aspects: subdividing customer groups, so as to take unique actions for each group; Use big data to simulate the real situation, explore new needs and improve the return on investment; Promote the sharing of big data achievements of relevant departments and improve the return on investment of the entire management chain and industrial chain; Innovation of business model, products and services.
The earliest story about big data happened in Target, the second largest supermarket in the United States. Pregnant women are a customer group with high gold content for retailers. But they usually go to specialized pregnant women's stores instead of buying pregnant products at Target. When people mention Target, they often think of daily necessities such as cleaning supplies, socks and toilet paper, but ignore that Target has everything a pregnant woman needs. So what can Target do to intercept these customers from pregnant women's products stores? To this end, Target's marketers turned to Andrew Pole, senior manager of Target's customer data analysis department, and asked him to build a model to identify pregnant women who were pregnant for the second time. In the United States, birth records are public, and as soon as a child is born, newborn mothers will be surrounded by overwhelming product preferential advertisements. It will be too late for Target to act again, so it is necessary to act when the pregnant woman is pregnant for the second time. If Target can know which customer is pregnant before all retailers, the marketing department can send them tailor-made preferential advertisements for pregnant women and identify valuable customer resources early.
However, pregnancy is very private information. How can I accurately judge which customer is pregnant? Andrew Bohr thinks the target has a registration form for the baby shower. Andrew Bohr began to model and analyze the customer consumption data in these registration forms, and soon found many very useful data patterns. For example, the model found that many pregnant women would buy a lot of odorless hand cream in large packages at the beginning of the second pregnancy; 20 weeks before pregnancy, buy a large number of high-quality tablets and other health care products that supplement calcium, magnesium and zinc. Finally, Andrew Bohr selected the consumption data of 25 typical commodities and constructed the "pregnancy prediction index". Through this indicator, Target can predict the pregnancy of customers within a small error range, so Target can send preferential advertisements for pregnant women to customers in advance. According to Andrew Pole's big data model, Target made a brand-new advertising marketing plan, and as a result, Target's sales during pregnancy showed explosive growth. Andrew Bohr's big data analysis technology has been extended from pregnant women to other sub-customers. From Andrew Pole joining Target in 2002 to 20 10, Target's sales increased from 44 billion dollars to 67 billion dollars.
Big data has been used to integrate marketing communication.
As of the third quarter of 20 13, the number of netizens in China reached 608 million, and the Internet penetration rate was 45.4%. In 20 13 years, the number of cmnet netizens reached 652 million; By the end of 20 13, 1 1, the transaction volume of e-commerce reached 9.7 trillion yuan, up 34.6% year-on-year.
Undoubtedly, hundreds of millions of Internet and mobile Internet users have created and will continue to create massive amounts of data, and the era of big data has arrived. From the daily behavior and related research data, we can deeply understand the changes that information technology has brought to people's behavior: the habits that people have formed for decades, such as a newspaper, a teahouse party and a TV news broadcast, have been and are being replaced by search engines, news portal browsing, mobile phone clients and social behaviors of Weibo, WeChat and self-media; The data devices around us have also changed from one or two computers and mobile phones to three or five smart devices per person; The capacity of U disk changed from 32MB to 8G; The number of pixels of digital cameras has changed from 6,543,800+0,000 to 6,543,800+0,600; The data volume of a photo has changed from 100 KB 10 years ago to 2MB of the current iPhone photo. The rapid development of science and technology is dazzling. ADSL has been replaced by fttp, 2G is outdated, 4G is being deployed in full swing, and e-commerce and logistics, and even the Double Eleven Consumer Festival, have become clear symbols in the era of big data.
A feature of the era of big data is that great changes have taken place in daily life. People's understanding of society, the methods of contact and communication with other members of society are changing with each passing day, the means are rich and colorful, and the content is changing rapidly. The consumer behaviors that marketers care about become clear and transparent as these behaviors become more and more complex and diverse; Although consumers rely on these devices and means, they also provide clear clues for suppliers of products and services. For example, a mobile phone, from power-on to power-off, keeps getting information in the operator's network, and also tells the operator's system its position, signal strength and busy and idle state; Through the analysis and utilization of this information, the operator can know when and where the customer started (the habit of getting up and the place to spend the night); When and where to accelerate or transfer to the subway base station (driving a taxi or taking a bus); Office or customer location (where you stay in the morning or even all day); Number of family members, relatives and friends (number of families and call frequency); Mode and frequency of social activities (place and frequency of dinner time, place and destination before midnight); In a sense, operators will know this customer better than their colleagues, bosses and even spouses. He even knows some behaviors and habits that he doesn't know.
Big data has brought unprecedented opportunities to integrated marketing communication activities. The richness, timeliness and accuracy of consumer behavior data bring convenience to the collection, analysis and processing of our enterprise. If we can collect, integrate and analyze consumers' online records, browsing records, location information, group buying interests, online shop browsing records and online shopping records, enterprises can get a perfect and clear consumer behavior map, which will bring convenient tools, perfect means and good results for consumers' insight.
Nike's integrated marketing based on big data has achieved tangible results. Young consumers in China are crazy about running feedback. Running is still Nike's largest sports category in China, higher than basketball and football. In Nike's global performance in fiscal year 20 13, the contribution of running accounted for 20.46%, second only to sportswear's 26.99%. This is why Nike is so confident in running. Of course, what is more critical is the sports social network extended from the NIKE+ platform. Nike's business model is also changing with the extension of this platform: "The key for Nike to launch the digital marketing model of Nike+ platform is to enhance the interaction with consumers and realize more personalized service to customers through the detection and processing of consumer information. This is an innovation in consumer demand services, which is conducive to businesses to understand and master consumer information more conveniently and quickly, and is also conducive to their targeted launch of products. " Technological innovation has also promoted the emergence of Nike's new business model, namely "user+data+service+terminal". Through the digital innovation of Nike+, Nike is breaking away from the traditional sports enterprises that manufacture and sell shoes. In this emerging mode, Nike+ is like the nervous system, connecting various sports products with users. Nike can provide users with online and offline professional sports services (such as night running, category experience stores, marathons and other offline activities) through the platform of Nike+ to enhance the sports experience and explore more business opportunities. For example, the fun run concept allows Nike's R&D personnel to add reflective elements to shoes and clothes, which directly promotes market sales.
Challenges and opportunities coexist.
Big data has also brought great challenges to integrated marketing communication activities: the richness of data has certainly brought clearer and more comprehensive behavior descriptions, but it has also brought consistency problems. Multi-source data can describe details from various aspects. However, assembling panoramic pictures of consumer behavior has become a more technical, detailed and complicated task, which requires more level and ability. Data experts, behavioral and psychological experts need to participate more in the analysis work and play a greater role.
Countermeasures of marketing activities: Before the era of big data, it was difficult for integrated marketing communicators to obtain consumer data with wide geographical area, comprehensive characteristics, timely feedback, accurate content and strong interaction. One-sided, slow and non-interactive is the state of most behavioral data. Marketers should deeply understand the background environment and channels for the generation and processing of these data, actively and cautiously make good use of these data, and turn consumer insight into the ability to promote consumer interaction, deliver products and services that consumers need, and continuously enhance consumer loyalty, so as to become active consultants and effective practitioners of corporate strategy.
For example, in the process of consumer-centered marketing, marketing organizations exchange information as truly and openly as possible to provide consumers with practical, knowledgeable and interesting content, which is also called content marketing. Such efforts can not only improve brand awareness, establish brand authority and ideological leadership, but also win the favor of consumers and form their loyalty to the brand. Michelin Guide, published in the early 20th century, is a practical information guide for consumers in go on road trip, France, such as scenic spots and restaurants. Later, it developed into the most authoritative food and beverage evaluation reading material in the world, and was deeply loved for its authority, interest, education and practicality. Therefore, valuable content will promote long-term communication between brands and consumers.
Big data analysis will become the core competitiveness of marketers. How to effectively use data, the most valuable enterprise asset, has become an important topic for marketers. Data analysis will make marketing investment more effective, and it can also play a vital role in predicting consumer behavior. Any of its achievements have been the dream result of marketers. Data analysis will help brands stand out in the environment of economic globalization, homogenization of competition and heavy commercialization of products and services. Nevertheless, many enterprises still have a long way to go. The adjustment of organizational structure to adapt to the consumer-centered enterprise strategy, the lack of talents who are good at using data and the lack of big data analysis technology are all great challenges that enterprises generally face.
The above is what Bian Xiao shared for you about integrated marketing meeting big data. For more information, you can pay attention to Global Ivy and share more dry goods.