Why Marketing Analytics Hasn’t Lived Up to Its Promise
Carl F. Mela and Christine Moorman, MAY 30, 2018
[Time 2]
We see a paradox in two important analytics trends. The most recent results from The CMO Survey conducted by Duke University’s Fuqua School of Business and sponsored by Deloitte LLP and the American Marketing Association reports that the percentage of marketing budgets companies plan to allocate to analytics over the next three years will increase from 5.8% to 17.3%—a whopping 198% increase. These increases are expected despite the fact that top marketers report that the effect of analytics on company-wide performance remains modest, with an average performance score of 4.1 on a seven-point scale, where 1=not at all effective and 7=highly effective. More importantly, this performance impact has shown little increase over the last five years, when it was rated 3.8 on the same scale.
How can it be that firms have not seen any increase in how analytics contribute to company performance, but are nonetheless planning to increase spending so dramatically? Based on our work with member companies at the Marketing Science Institute, two competing forces explain this discrepancy—the data used in analytics and the analyst talent producing it. We discuss how each force has inhibited organizations from realizing the full potential of marketing analytics and offer specific prescriptions to better align analytics outcomes with increased spending
The Data Challenge
Data are becoming ubiquitous, so at first blush it would appear that analytics should be able to deliver on its promise of value creation. However, data grows on its own terms, and this growth is
often driven by IT investments, rather than by coherent marketing goals. As a result, data libraries
often look like the proverbial cluttered closet, where it is hard to separate the insights from the junk.
In most companies, data is not integrated. Data collected by different systems is disjointed, lacking variables to match the data, and using different coding schemes. For example, data from mobile devices and data from PCs might indicate similar browsing paths, but if the consumer data and the data on pages browsed cannot be matched, it is hard to determine browsing behavior. That’s why understanding how data will ultimately be integrated and measured should be considered prior to collecting the data, precisely because it will lower the cost of matching.
What’s more, most companies have huge amounts of data, making it hard to process in a timely manner. Merging data across a vast number of customers and interactions involves “translating” code, systems, and dictionaries. Once cohered, vast amounts of information can overwhelm processing power and algorithms. Many approaches exist to scale analytics, but collecting data that cannot be analyzed is inefficient.
[432 words]
[Time 3]
An irony of having too much data is that you often have too little information. The more data and
fields collected, the less they overlap, creating “holes” in the data. For example, two customers with the same level of transactions could have very different shares of wallet. While one represents a selling opportunity, the other might offer little potential gain. Data should be designed with an eye towards imputation — so the holes in the data can filled as needed to drive strategy.
Perhaps worst of all, data is often not causal. For example, while it is true that search advertising can be correlated with purchase because customers are in a motivated state to buy, it does not follow that ads caused sales. Even if the firm did not advertise, consumers are motivated to buy, so how does one know whether the ads were effective? Worse, as data grows, these problems compound. Without the right analytic approach, no amount of investment will translate to insights.
Companies should do two things to harness the power of analytics in their marketing functions. First, rather than create data and then decide what to do with it, firms should decide what to do first, and then which data they need to do it. This means better integrating marketing and IT, and developing systems around the information needs of the senior management team instead of creating a culture of “capture data and pray.”
Second, companies should create an integrated 360-degree view of the customer that considers every customer behavior from the time the alarm rings in the morning until they go to bed in the evening. Every potential engagement point, for both communication and purchase, should be captured. Only then can firms completely understand their customers via analytics, and develop customized experiences to delight them. The CMO Survey we referenced above shows that firms’ performance on this type of integration has not improved over the last five years, challenging companies’ ability to answer the most important questions about their customers.
[334 words]
[Time 4]
The Data Analyst Challenge
The CMO Survey also found that only 1.9% of marketing leaders reported that their companies have the right talent to leverage marketing analytics. Good data analysts, like good data, are hard to find. Sadly, the overall rating on a seven-point scale, where 1 is “does not have the right talent” and 7 is “has the right talent,” has not changed between the first time the question was asked in 2013 (Mean 3.4, SD =1.7) and 2017 (Mean 3.7, SD =1.7).
The gap between the promise and the reality of analytics points to a disconnect that needs resolution. Companies need to better align their data strategy and data analyst talent to realize the potential that analytics can bring to marketing managers. In the absence of talent, even great data can lie fallow and prevent a firm from harnessing the full potential of the data. What are some of the characteristics that companies should look for in good data scientists? They should:
Clearly define the business problem. Managers who rely on data scientists to know what might be possible to do with the data often find great value in simply having that person help define the problem. For example, a marketer coming to a data analyst asking questions about driving conversions might not realize that there’s also data at the top of the purchase funnel that might be even more germane to driving long-term sales. Rather than taking requests as they are stated, data analysts should take requests as they should be asked, integrating advice tightly with the needs of the company. For example, a request to assess how marketing promotions affect sales should also account for the effect of promotions on brand equity.
Understand how algorithms and data map to business problems. Companies will see more effective data analytics if teams are clear on firm objectives, informed of the strategy, sensitive to organizational structure, and exposed to customers. To enable this understanding, data analysts should spend physical time outside of data analytics, perhaps visiting customers to give them an understanding of market requirements, attending market planning meetings to better appreciate the company’s goals, and helping to ensure data (IT), data analytics, and marketing are all aligned.
[370 words]
[Time 5]
Understand the company’s goals. Data analytics is beset by multiple requests, like a waiter serving too many customers. A clear recognition of a firm’s goals enables data analysts to prioritize projects and allocate time to those that are the most important (those that have the highest marginal value to a firm). Requests should be centralized, and then prioritized by a) whether the findings have the potential to change the way things are done and b) the economic consequences of such changes. Several companies develop standardized forms to ensure requests are assessed on an equal footing. An attendant benefit of this process is that it mitigates the potential for opportunistic research clients to approach analysts asking them to conduct a study to support a preconceived strategy for political reasons, instead of deciding between strategies that are in the best interests of the firm.
Communicate insights, not facts. Communication theory tells us that the transmitter and receiver of information must share a common domain of knowledge for information to be transmitted. This means analysts need to understand what the firm’s managers can understand. Small font sizes, complex figures and equations, the use of jargon, and an emphasis on the modeling process instead of insights and explanations are common errors when presenting analyses. Why should one use a complicated model to present information when a simple infographic would suffice? Presentations should be organized around insights, rather than analytic approaches. This is another reason it is critical for analysts to connect externally with customers and internally with the managers using their work. Plus, instead of reporting a “parameter estimate,” an analyst should communicate how results point to tangible strategic actions. This requires analysts to structure their analysis in a decision framework that helps managers assess best and worst case scenarios.
Develop an instinct for mapping the variation in the data to the business questions. That means two things. First, analysts need a comprehensive understanding of all the relevant drivers (e.g.,
marketing and environmental factors) and outcomes (e.g., purchase funnel metrics). For example, to ascertain the effect of advertising on sales, one would need to recognize that concurrent changes in product design can affect sales, lest one misattribute the effect of product changes to the advertising that announces them. Second, analysts must have a means to ensure that drivers lead to outcomes instead of outcomes leading to drivers. Once again, this requires the analyst to understand the nature of the markets being analyzed. Regarding the latter, no complicated model that purports to control for missing information can ever compensate fully for lack of causal variation. Likes drive sales and sales drive likes. However, disentangling the two means having some factor that can independently manipulate one and not the other.
[454 words]
[Time 6]
Identify the best tool for the problem. On the analytics side, it goes without saying that years of training and practice are necessary. One cannot play an instrument without learning it, and the same is true for analysts. Most important is knowing which tool, of the many available, is best for which problem. At a very granular level, experimental methods are especially adept at assessing causality; supervised machine learning excels at prediction where non-supervised machine learning can decompose non-numerical stimuli into tags or attributes for further analysis. Economics and psychology afford deep insights into the nature of consumer behavior, and statistics can help us excel at inference. A strong understanding of marketing grounds all of these tools and disciplines in the business context necessary to produce effective advice.
Span skill boundaries. Some marketing analysts excel at math and coding, and some excel at framing issues, developing explanations, and connecting to business implications. A far smaller set excel at both. Companies either need to wrap these variegated skills into one person through training and accumulating different types of experiences, or, more likely, assemble a team that is sufficiently facile with the techniques that they can interact productively, ensuring that there is some mechanism to match the approach (and the analyst) to the problem. This match requires senior talent, with the breadth of perspective to align analytical resources and business problems.
In light of the exponential growth in customer, competitor, and marketplace information, companies face an unprecedented opportunity to delight their customers by delivering the right products and services to the right people at the right time and the right format, location, devices, and channels. Realizing that potential, however, requires a proactive and strategic approach to marketing analytics. Companies need to invest in the right mix of data, systems, and people to realize these gains.
[303 words]
Source: Harvard Business Review
https://hbr.org/2018/05/why-marketing-analytics-hasnt-lived-up-to-its-promise
Part III: Obstacle
5 Surprising Findings About How People Actually Buy Clothes and Shoes
Jeremy Sporn & Stephanie Tuttle, JUNE 06, 2018
[Paraphrase 7]
Retail has been constantly reinventing itself, and participants race to keep up with what feels like a series of epic shifts in consumer preferences. Apparel brands are investing especially heavily in online shopping capabilities and introducing interactive features that complement apps and websites. Retailers and manufacturers are rushing out new products to keep pace with the leaders of fast fashion such as Zara, H&M, and Forever 21, which launch new fashions every week or so.
But do consumers actually crave all of these changes? And which approaches can generate growth in this changing environment? Many manufacturers try to answer these questions using point-of-sale data, which often comes filtered by the retailers that gather the information; media coverage, which tends to focus on the new; and previous sales of their products, which reflect the past.
To get a clearer, more-complete picture, we studied actual decisions made by 1,500 apparel and footwear shoppers in the United States. We asked them about everything from their initial motivation to shop, to the shopping journey itself, to how they felt after making their purchases. The results showed that retailers need to look beyond the buzz surrounding retail and instead focus on specific aspects of consumer behavior if they hope to improve their businesses and drive growth. According to our research, many assumptions about the ongoing revolution in retail are, in fact, myths.
Below are five of our most surprising findings.
Myth: Shopping has become truly omnichannel.
Fact: Most journeys are still overwhelmingly single-channel, though this is changing.
The buzzword of recent years has been omnichannel, meaning that consumers are thought to combine store visits and online interactions during their shopping journeys. However, while omnichannel is growing in importance, our study suggests that 83% of shopping journeys still happen within a single channel — overwhelmingly in traditional stores, which account for almost
80% of apparel purchases today.
Apparel companies and independent retailers need to continue to focus on making their physical
stores attractive to visit so that they ultimately become more-effective contributors to brands’
financial results. One area where they trail is knowledge of the customer. Online sites have a wealth
of data on shoppers — from their tastes, as indicated by previous purchases, to demographic
information and preferences. In a store, however, a sales associate tries to guess a shopper’s tastes in real time. As a solution, technology could be used to customize the in-store experience by
encouraging customers to swipe their smartphones as they enter, so that their profile could then be used to tailor the experience and offers. Stores should also make shopping memorable — through drinks and other hospitality services, for example — and encourage customers to complete their transactions online.
Myth: The sales channel doesn’t matter.
Fact: When consumers purchase online, they tend to buy more.
Shopping journeys concluding in online purchases have baskets that are 25% larger, on average.
When someone first visits a physical store and then purchases online, the effect is even more
pronounced: Baskets are 64% larger.
One reason why is that free shipping often comes with a minimum basket size, so customers often
select extra items to reach this threshold. Another is the greater product range available online
without the need to carry inventory in a prime-location store. Moreover, it is relatively easy to create impulse purchases online from information gathered about the shopper and to incentivize larger basket sizes.
To benefit, retailers with physical stores should make a concerted effort to drive in-store customers
to their website to make their purchases. This is what Bonobos does: It encourages shoppers to
experience a product in its “no-inventory” stores and then complete the order online.
Myth: Online shopping is about instant gratification.
Fact: Online journeys tend to be longer than in-store.
Shopping with clicks sounds like a speedy process. But consumers actually take more time online
than when shopping in physical stores, and they make more stops. In fact, 57% of shopping journeys that conclude with an online purchase begin with a consumer either first looking at another website (29%), visiting a brick-and-mortar store (15%), or both (13%) before ultimately transacting online through any particular retailer. The other 43% of journeys that conclude with an online purchase are one-stop journeys that begin and end with the same online retailer.
This means that online shoppers are doing a lot of comparison, so online retailers should work harder to close sales quickly while they have the attention of the consumer. They can do this by actively sending cart recovery messages or creating loyalty programs for a particular site. Removing hassles could help: More than 10% of consumers indicated that they had abandoned a cart on a website and then bought the items elsewhere simply because they didn’t like the first site’s shipping or return policy.
Myth: The retailer doesn’t matter.
Fact: Spend is dramatically higher at brand stores and websites than in multi-brand stores.
Direct-to-consumer brand stores and websites generate revenues 86% higher than purchases of those same brands elsewhere — and, of course, better margins. A specific store or site may make a brand feel more valuable and differentiated to customers, inducing them to spend more than they would otherwise. Direct-to-consumer channels also help to develop (or maintain) a brand’s image.
This means brands could benefit from driving consumers toward their own sales channels, though of course in a way that does not jeopardize their relationships with retail channel partners such as
department stores. One important strategy is stocking unique products available only from a brand’s website or store. Nike has had some success with this strategy, and has gone one step further by allowing people to customize their products on its website. Other apparel brands might seek to attract customers through differentiated and personalized products.
Myth: Consumers always want something new.
Fact: Very often, they are happy to rebuy the same or a similar item.
Fast fashion has become a buzzword for apparel makers, but many consumers are simply looking to replace an item they already have. This is especially true in intimates and basics, but also in fashion, where the aim of 83% of shopping journeys is repeat purchases, and athletic products (87%).
Brands should, to some extent, change their mindsets: Success could mean finding consumers that like a product and reselling it to them, and not always trying to reinvent the wheel. The customer, not the product, should come first. Repurchasing of products previously bought could be made easier through promotions on similar items, tailored advertising for new versions, reminders when product likely needs to be replaced, and even subscription services. To get to know customers better, brands might encourage them to go online during trips to physical stores and to comment on products through apps. Manufacturers can then follow up and encourage repeat purchases.
Acting on these findings implies going beyond technology fixes and making changes to a company’s
organizational structure. At a minimum, physical stores and e-commerce operations would benefit
from better links. For example, internal financial systems need to accurately reflect a customer’s
buying an item online and later returning it to a physical store. Eventually, brands and retailers
should integrate their e-commerce units into the rest of their commercial organizations, replacing
channels that compete for sales from the same customer with a structure that puts the customer first. In the future, customers will decide where and how they shop, and the apparel business must make this as smooth — and profitable — as possible.
[1236 words]
Source: Harvard Business Review
https://hbr.org/2018/06/5-surprising-findings-about-how-people-actually-buy-clothes-and-shoes
作者: FrancesF 时间: 2019-10-25 17:25
T2 2'46
live up to 不辜负；符合；遵守
At first,the article listed numbers to show 2 things:
1. the companies that plan to increase analysts- increase
2. the effectiveness of analysts- remain stable
Why so many co. still plan to increase analysts?-2 things
1. data
2. talent
Data challenge
1. data cannot assure the value.
ubiquitous adj.无处不在的；十分普遍的
blush 脸红；尴尬
这一小点不是很懂~是不是说data是自己在发展的，跟管理投资决策无关，但是跟IT 投资有关-查
2. not integrated
3. huge amounts-cannot be produced in a timely manner
T3 1'50
irony: to much data, little info-e.g.
not causal-这里又不是很懂了，causal因果联系，
data is not causal-need correct approach to corrlate the data
what co need to do?
At first, need to figure out what information they need for data to provide
Second, carry out a 360 analysis of customers
T4 2'06
The talent challenge
List some numbers
many co lack talented analysts
The factors of a good talent
1. understand the co's need
2. not only limited to the data processing, align the mkt, customer, data
harnessing
T5 2'51
Understand the co's goal
communication insight
这一段的总起句没有看懂
1.understand all the drive factors and outcomes
2. have a means of .. drive factors lead to the outcomes, not outcomes lead to the drive factors
instinct有。。。的直觉 原来是培养从数据映射到业务模式的直觉
At first, the passage states a paradox: Companies’ investment in marketing analytics is increasing in recent years despite the outcomes are not positive.
Then, the author presents two reasons to close the gap:
1. Data challenge.
2. Talents of analytics. (没太理解)
[Time 3] 3’15’’ 103wpm
Many companies have so much data because they have too little information.
Data is not causal.
Advice:
1. Companies should decide what they want to do first and what data they need then.
2. Companies should create a 360-degree view of customers
[Time 4] 3’06’’ 119wpm
the data challenge:
Companies can’t find good data analytics
The standards of good data analytics:
1. A clear understanding of the problem
2. A good understanding of the algorithms and data maps( the subtle business logics)
[Time 5] 3’55’’ 116wpm
how( analysts)
what a data analyst should do:
1. Understand the company’s goals and set priorities.
2. Communicate the insights, not merely the facts
3. Map the variations( what are the divers; and 逻辑关系的顺序)
[Time 6] 2’30’’ 121 wpm
how（company）
1. Find the right method （different approaches for different skills）
2. Span skill boundaries.
总结：There is a long way to go…
作者: 加油鸭！ 时间: 2019-10-25 22:34
T2: 03:50
larger percentage of budget is allocated to market analytics, but actually marketing analytics increased little these years.analyzing data is more important and cheaper than collecting data.
T3: 03:18
more information may mean more holes. First, you need to know how ads cause effect. decided what to do before collect data. Second, figure out what consumer want and capture their behavior.
T4: 02:23
less tanlent analytics
Clearly define the business problem.
Understanding how data map to the problem.
T5: 03:26
understanding goals;communicate insights; develop instincts;
T6: 02:45
identify the best tool; span the skill; 作者: mrbeann 时间: 2019-10-26 00:06
Time2 4’32’’
The marketing budgets using in the data analytics are estimated to increase hugely. Integrating data seems to be more important than collecting data.
Time3 2’36’’
More data does not mean more benefits because too much data brings holes. Companies should decide what they need first to integrate marketing and data analysis. What is more, they are supposed to create holistic view of their customers.
Time4 2’45’’
Good data analysts are deficient, just like good data. It i very important for a company to find a good data analyst. And good data analysts have some hallmarks, which include the capability of define the business problems and of using codes to fickle with the problems.
Time5 3’38’’
They also need to comprehend the goals and priorities of the company, communicate with managers insights and have an instinct for coalescence of data and business problems.
Timr6 2’54’’作者: chyuee 时间: 2019-10-26 00:25
Time2：3’56（432）
讲述analyse投入与满意度不成正比，并且阐述原因
Time3：3’11(334)
Data多的坏处以及应该怎么做
Time4:2’50（370）
关于分析师的一些不足以及应该怎么挑选分析师
Time5:3’24
Time6:1’45
剩下的内容都是4的衔接，bullet point怎么做
Time7：关于零售商的一些调查，有5高myth以及fact作者: Feifeissss 时间: 2019-10-26 02:14
Time 2 01:55
Time 3 02:49
Time 4 02:33
Time 5 01:47
Time 6 02:17
Obstacle 07:38作者: van7要考650 时间: 2019-10-26 02:36
Time 2 04:15.08
为什么市场调研不能达到效果？
市场调查的投资增长从5.8-17.3% 但是效果不行3.8-4.1
俩个原因
I The Data Challenge
1、信息增多是由于科技投入，而不是因为相应的市场目标，导致信息看起来很杂乱，不能从中提取有深度的内容
2、数据没有被整合，不同系统手机，缺少匹配信息和用不同编码组合。
3 数据过度，没有及时整理，只是收集没有被分析是无效的。
Time 3 02:26.55
数据多信息少，重叠少，创造黑洞少，黑洞能有利于制造策略
最差的是数据不是有因果联系的。需要正确的分析手段。
公司需要做俩件事，一决定什么先做，需要哪些数据。二创造全方位的消费行为调查。这样才能了解消费者，制造独一无二的消费体验。
Time 4 2:52.70
II原因 The Data Analyst Challenge
信息没有用，没有正确的本领利用市场分析。需要align data strategy and data analyst talent.需要找什么样的信息处理人员呢？
Clearly define business problem
Understand how algo and data map to business problems
Time 5 3:32.24
Understand the company’s goals
Communicate insights, not facts
Develop an instinct for mapping the variation in the data to the business questions: 1 understand all relevant drivers 2 have a means to ensure that driver lead to outcomes instead of outcomes leading to drivers. 在复杂的模式都不能弥补缺失因果变量
Time 6 2:23.84
Identity the best tool for the problem
span skill boundaries
Obstacle
12:02.36
这边文章结构很清晰，5个发现-原因-建议（有的包含小案例）。 最后来个总结作者: 云栈 时间: 2019-10-26 02:49
T2 3min5s
The number of data analytics has increased; however, satisfaction stays the same.
T3 1min58s
The company should decide what they want firstly, then collect the data. Also, they need to analyze the data related to everything about their customers.
T4 2min1s
A good data analyst is also important. The company should clearly define their business problems and also better understand the algorithm and data map to business problems.
T5 3min33s
a data analyst needs to understand the company’s goals.
The need to communicate with customers and managers.
They need to map different data to the business question.
T6 1min57s
They also need to identify the best tool to solve the problem.
Also, they need to span skill boundaries.
To develop the potential, the company needs to find the right data, systems, and people.
P7 7min41s 作者: stan152 时间: 2019-10-26 10:56
time2(01:47.51)
ubiquitous无处不在的
proverbial cluttered closet总所周知杂乱的衣橱
time3（01:41.14）
algorithms算法
time4（01:25.78）
leverage利用
time5（01:37.16）
time6（01:33.42）
At a very granular level在一个非常细的层面
obstacle（07:23.34）
apparel 服装
调查研究人们的购物行为
提出了五点误解和事实作者: 洛阳洋洋999 时间: 2019-10-26 13:16
Time 2: 【03’46’28】
虽然市场分析的投资增长明显但是市场分析所带来的的效果没有显著增长，究其原因，有两方面的影响：分析所用的数据，和分析师的才能。数据量大，杂乱并且不同系统数据不匹配。
Time 3: 【02’13’38】
数据的另外两个问题：重合度低，因果关系复杂；对于市场分析有两个建议：公司应该首先确定目标，再收集数据；公司应该对于顾客进行全方面的分析。
Time 4: 【03’03’94】
另一个影响因素是缺少数据分析相关的人才。这方面有几个建议是：1.让分析师定义问题；2.分析师应该更多的参与到公司决策讨论，顾客走访等非分析的工作。
Time 5: 【02‘59‘11】
3.分析师应该了解公司战略，从而能够给众多的分析需求进行优先级排序；4.用更加直白的解释去呈现分析结果；5.形成用数据的变化去解释商业问题的直觉
Time 6: 【01‘56‘28’’】
6.分析师要懂得使用最有效的工具去解决特定问题；7.拓宽自己的知识领域；8.公司要根据自己的需求配置合适的战略人员系统等资源
Obstacle 【08’15’62】
近些年来，鞋服行业飞速发展，时尚风向变化迅速。通过研究是什么激发了人们的购买欲望时，发现了人们五个错误认知：
1. 顾客现在更喜欢多渠道购物，数据显示，目前大部分人仍然是选择单一渠道购物，并且大部分还是选择线下购物
2. 购买渠道不重要，顾客在网上的购物消费会更多，因为在网上，商品种类更多，更容易激起顾客购买冲动，同时网上有一些购买的门槛，顾客需要凑够一定的金额才能下单
3. 顾客在网上的购买速度更快，实际上顾客在网上会花费时间对进行不同商品的对比，总的购物市场会更久
4. 零售商是哪家不重要，实际上顾客更加倾向于在品牌的直营店购物而不是多品牌的经销店。为了保持和经销商的关系，有个别款式会只在直营店售卖
5. 顾客更喜欢新的商品，实际上顾客更喜欢购买原来的商品或者类似的商品
商家可以结合这些发现来更好促进消费者购物。 作者: skin-y 时间: 2019-10-26 17:15
Time2: 12’24 [432 words]
marketing budgets will have a huge increase while performance impact increased slightly - not match - two explanation - the data and the analyst talent
the problem in data:
hard to find insights
not integrated
huge amounts and hard to analyze
Time3: 6’55 [334 words]
the problem in data:
data↑，information↓ → should be designed with an eye
data is not causal → need right analytic approach
how to improve?
decide what to do first and then collect data
integrate customer data and know more about costumers
Time4: 5’45 [370 words]
the problem in data analyst:
good analysts are hard to find
the gap between promise and the reality of analytics points
the traits about good data scientists:
clearly define the business problem
understand how algorithms and data map the business
Time5: 4’49 [454 words]
the traits about good data scientists:
understand the company’s goals
communicate insights
map the variation in the data to the business questions
Time6: 4’40 [303 words]
the traits about good data scientists:
identify the best tool for the problem
span skill boundaries
conclusion：right data and good analysts are important, companies need to invest in these area
Paraphrase7: 20’00 [1236words]
five myths and facts about retail
combine visits → physical stores are more → improve physical stores
sales channel is not important - online store, buy more → experience products in the physical store and complete order online
time spending in online shopping is short → longer than in-store → sales quickly and remove hassles
retailer is not important - important - provide unique products only in the brand’s website or store
want something new - rebuy the same item - encourage repeat purchases
Obastacle
10’49 用户对服饰购买的行为调查
1）一站式
重视线下用户收集
2）线上买的更多
线下向线上转化
3）线上的购物时间更长
用户比较
4）品牌直营店购买更多
5）单品重复购买 作者: lavande505 时间: 2019-10-30 11:33
T2 3'02
T3 2'54
T4 3'22
T5 4'21
T6 2'01
obstacle 10'27作者: cycs 时间: 2019-10-30 16:04
2. The investment in marketing analytics is expecting 198% increase in the next three years while the linkage between marketing analytics and company performance is still modest as before. There are two reasons according to the alliance companies of the author are data itself and people who produce it. The challenges of realizing full potential of marketing analytics from these two aspects are as follow. Data: data source, the discrepancy of IT systems in the company, fields to collect.
3. 1:51 The suggestion to tackle issues mentioned above in Data, author suggested that company should plan what they want to collect ahead and should consider collecting the whole process of consumer’s behavior in order to get holistic view and provide customized service.
4. 2:15 the right data analyst is a continuous issue that the marketing department faced. Other than looking for a talent, the most important things that analyst should do or be requested are define the problem and provide solutions linked to business, and well understand company’s strategy and goals and market trend and requirement.
5. 2:20 Analysts should have two important competencies: market insight and business sense (acumen)
6. 1:48 Analysts need not only equip technical skills but also psychology, statistics, and market field. In addition, they need to have a continuous learning mindset. Without having right mix of IT, people, data, it is not possible to realize the gains expected from marketing analytics.
7. 8:04 obstacle 作者: -sarahsha 时间: 2019-11-25 10:14
Day51- No.2592 经管
7-5'08"- 没有读细节；读了大标题及解释，更多的内容只是带着看...
网店如火如荼，品牌怎么还到处开新店。本文介绍某一研究的发现，或可解释该现象：
1. 顾客并不都是在实体店铺试衣，又到网上购买，多的人只选择一条途径；
2. 消费渠道很关键，网购倾向购买更多；
3. 网购事实上花费更多的时间和心思；
4. 消费者在品牌店铺与网站的整体花费高于单一品牌店铺；
5. 消费者并不都喜欢新鲜玩意，很多人会重复购买同一间商品。作者: Ivy要上750 时间: 2019-12-11 00:36
Time 2 6:01
Time 3 3:20
Time 4 3:35
Time 5 3:41
Time 6 3:04作者: 驱动力 时间: 2020-7-30 01:02
T2 2:16
T3 1:56
T4 2:32
T5 2:19
T6 1:53
Obstacle 9:42作者: vivi16_Yu 时间: 2020-7-31 01:18
OB：11’12[1236W]作者: はな 时间: 2020-7-31 21:38
P7:7:36
physical stores can not be replaced by e-commence bussiness.Moreover,data collected on the internet help the giant to tailor their products.