In the beginning, the case was just data for a company that did not indicate any useful information that would help decision-makers. In this case, I had to ask questions that could help extract and explore information that would help decision-makers improve and evaluate performance. But before that, I did some operations in the data to help me to analyze it accurately: 1- Understand the data. 2- Clean the data “By power query”. 3- insert some calculation and columns like “COGS” cost of goods sold by power query. 4- Modeling the data and adding some measures and other columns to help me in analysis. Then I asked these questions: To Enhance Customer Loyalty What is the most used ship mode by our customer? Who are our top 5 customers in terms of sales and order frequency? To monitor our strength and weak points Which segment of clients generates the most sales? Which city has the most sales value? Which state generates the most sales value? Performance measurement What are the top performing product categories in terms of sales and profit? What is the most profitable product that we sell? What is the lowest profitable product that we sell? Customer Experience On Average how long does it take the orders to reach our clients? Based on each Shipping Mode

Then started extracting her summaries and answers from the pivot tables and designing the data graphics in a dashboard for easy communication and reading of the information as well. And after completing these operations, I made some calculations related to the KPI to calculate the extent to which sales officials achieved and the extent to which they achieved the target.

UAB "ASK linija" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

This study examines people's perceptions of the entrepreneurial process, the various sources of help and hindrance that beginning businesses encounter, the impact of new businesses on the economy, and the experiences of individuals involved in creating new businesses. Representative adults in Wisconsin were asked for their opinions regarding entrepreneurs and business opportunity, and were also asked about their own backgrounds and careers, and about any entrepreneurs in their own families or social networks. Individuals identified as entrepreneurs during the representative adult interview were asked about their knowledge of public and private sources of assistance for small business, their reactions to the state and local infrastructures, sources of financing they may have employed, start-up problems, products or services offered, and the nature of ownership of their business. Owners and managers of new firms that were identified by new unemployment insurance filings were asked about past and current sales, job creation, out-of-state exports, current management focus, and future plans for their businesses.

The U.S. Big Data Pharmaceutical Advertising market is projected to be valued at $3.5 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 12.5%, reaching approximately $11.1 billion by 2034.

Days-of-Sales-Outstanding Time Series for IAC Inc.. IAC Inc., together with its subsidiaries, operates as a media and internet company worldwide. The company publishes original and engaging digital content in the form of articles, illustrations, and videos and images; and magazines related to women and lifestyle under the People, Better Homes & Gardens, Verywell, FOOD & WINE, The Spruce, allrecipes, BYRDIE, REAL SIMPLE, Investopedia, and Southern Living brands. It also operates a digital marketplace that connects home service professionals with consumers for repairing, remodeling, cleaning, landscaping, maintenance, and enhancement services under the Angi Ads and Leads, and Angi Services brands. In addition, the company operates websites that offers general search services and information, including Ask.com, a search site with a variety of fresh and contemporary content; Reference.com that offers content across select vertical categories; Consumersearch.com, which offers content designed to simplify the product research process; and Shopping.net, a vertical shopping search site, as well as offers direct-to-consumer downloadable desktop applications. Further, it provides Care.com, an online destination for families to connect with caregivers for their children, aging parents, pets, and homes under the Care For Business and HomePay brands; a platform to connect healthcare professionals with job opportunities under the Vivian Health name; The Daily Beast, a website dedicated to news, commentary, culture, and entertainment that publishes original reporting and opinion; and production and producer services for feature films for sale and distribution through theatrical releases and video-on-demand services under the IAC Films name. The company was formerly known as IAC/InterActiveCorp. The company is headquartered in New York, New York.

Einstellungen von Unternehmen zu Online-Aktivitäten. Themen: Unternehmen verkauft oder tätigt Einkäufe online und / oder nutzt elektronischen Datenaustausch (Electronic Data Interchange (EDI)); Online-Verkauf von Produkten oder Dienstleistungen über: eigene Website oder Apps, kleine Handelsplattform, große Handelsplattform, Transaktionen mit elektronischem Datenaustausch; prozentualer Anteil der Online-Verkäufe aus den folgenden Ländern an den gesamten Online-Verkäufen im Jahr 2014: Land, in dem das Unternehmen ansässig ist, andere EU-Länder, Länder außerhalb der EU; Länder, in denen 2014 online verkauft wurde; prozentualer Anteil der herkömmlichen Verkäufe aus den folgenden Ländern am herkömmlichen Umsatz im Jahr 2014: Land, in dem das Unternehmen ansässig ist, andere EU-Länder, Länder außerhalb der EU; Überlegungen oder Versuche, online in anderen EU-Ländern zu verkaufen; Unternehmen, die bereits Online-Handel praktizieren, wurden gefragt: Bedeutung der einzelnen folgenden Schwierigkeiten beim Online-Verkauf in andere EU-Länder: hohe Lieferkosten, hohe Kosten für Garantien und Rücksendungen, Unkenntnis der einzuhaltenden Vorschriften, mangelnde Sicherheit von Zahlungen aus dem Ausland, Urheberrechtsgründe, komplizierte oder hohe Besteuerung im Ausland, Notwendigkeit zur Anpassung der Produktetikettierung, mangelnde Sprachkenntnisse, Beschränkung oder Verbot von Verkäufen ins Ausland durch die Zulieferer, Verbot zur Nutzung einer Plattform Dritter durch die Zulieferer, Anordnung der Zulieferer zum Verkauf im Ausland zu anderen Preisen, Datenschutzangelegenheiten, unzureichende Interoperabilität, Spezifizität der Produkte, langsame Internetverbindung des Unternehmens oder der Kunden, hohe Kosten bei der Klärung von Beschwerden oder Streitfällen über Landesgrenzen hinweg; Unternehmen, die bisher keinen Online-Handel praktizieren, wurden gefragt: Bedeutung der einzelnen folgenden Schwierigkeiten beim Online-Verkauf: Mangel der nötigen digitalen Kompetenzen, Unkenntnis der einzuhaltenden Vorschriften, hohe Lieferkosten, hohe Kosten für Garantien und Rücksendungen, Beschränkung oder Verbot des Online-Verkaufs durch die Zulieferer, höhere Preise der Zulieferer für online verkaufte Produkte, Verbot zur Nutzung einer Plattform Dritter durch die Zulieferer, Risiko des Preisverfalls online verkaufter Produkte, Risiko der Imageschädigung von Unternehmen und Marken, langsame Internetverbindung des Unternehmens. Demographie: Angaben zum Unternehmen: Anzahl der Mitarbeiter, Gründungsjahr, unabhängig oder Teil eines nationalen oder internationalen Konzerns, Art der verkauften Produkte oder Dienstleistungen und Zielgruppe; Gesamtumsatz im Jahr 2014; Entwicklung des Umsatzes seit Januar 2012; prozentualer Anteil der Online-Verkäufe am Umsatz 2014; prozentualer Anteil des Online-Einkaufs am Gesamt-Einkauf 2014. Zusätzlich verkodet wurde: Befragten-ID; Land; NACE-Code; Unternehmensgröße; präferierte Interviewsprache; Nationengruppe; Gewichtungsfaktor. Companies’ attitudes towards online activities. Topics: company sells or purchases online and / or uses Electronic Data Interchange (EDI) type transactions; online selling of products or services using: own website or apps, small commercial platform, large commercial platform, EDI type transactions; percentage of the company´s online sales in 2014 coming from: country where company is located, other EU countries, countries outside the EU; countries in which online selling was available in 2014; percentage of the company´s non-online sales in 2014 coming from: country where company is located, other EU countries, countries outside the EU; considerations or attempts to sell online in other EU countries; companies already practicing online selling were asked: significance of each of the following difficulties regarding online selling to other EU countries: high delivery costs, expensiveness of guarantees and returns, ignorance of the applicable rules, insufficient security of payments from other countries, copyright reasons, complicated or costly foreign taxation, necessity to adapt product labeling, lack of language skills, restriction or interdiction by suppliers to sell abroad, interdiction by suppliers to use third platform for selling, request of suppliers to sell abroad at different prices, data protection issues, insufficient interoperability, specificity of products, slow internet connection of company or of customers, costs from resolving complaints and disputes cross-border; companies not yet practicing online selling were asked: significance of each of the following difficulties regarding online selling: lack of necessary digital skills, ignorance of the applicable rules, high delivery costs, expensiveness of guarantees and returns, restriction or interdiction by suppliers to sell online, higher prices imposed by suppliers for products sold online, interdiction by suppliers to use third platform for selling, risk of reduction in price for products sold online, risk of damaging image of company and trademarks, slow internet connection of the company. Demography: information about the company: number of employees, year of establishment, independent or part of national or international group, kind of sold products or services and target group; total turnover in 2014; development of turnover since January 2012; percentage of the value of sales in 2014 coming from online sales; percentage of the value of goods and services purchased online by the company in 2014. Additionally coded was: respondent ID; country; NACE-Code; company size; preferred language of the interview; nation group; weighting factor.

A 69-question survey instrument was developed by the study authors. Questions were selected based on the beliefs, values, and experiences shared by MLM participants in four focus groups conducted in an earlier phase of the research, and also on previous literature describing the characteristics of individuals and communities exposed to MLM. The survey was administered to participants belonging to the GfK KnowledgePanel, the largest online panel that uses probability-based sampling techniques for recruiting a nationally-representative sample of Americans. This sampling frame allows researchers to produce statistically valid estimates that are generalizable to the U.S. population.

The survey consisted of two phases: 1) initial screening of 7,949 KnowledgePanel members to calculate lifetime prevalence of MLM in the United States; and 2) the main survey with study-eligible respondents aged 18 years and older. Qualifying respondents who participated in phase 2 included 601 KnowledgePanel members who were distributors for an MLM firm sometime in the past (adopters), and 415 KnowledgePanel members who never participated in MLM before (non-adopters). Sample size quotas of 600 MLM participants and 400 non-participants were set a priori, although more panel members were screened into the survey to accommodate for case deletions due to response error and missing data.

For the 601 respondents who previously participated or currently work as distributors with an MLM firm, the survey instrument asked detailed questions about the first MLM firm they ever joined. They were asked to identify the name of the MLM company from a dropdown list (or free entry), who recruited them, the reasons why they joined, the average number of hours they worked each week, how long they maintained their membership, how much money they spent on inventory, training, and marketing materials, whether they made a profit, broke even, or lost money, and whether they felt the company accurately represented their chances of achieving financial success. The survey also gathered information on these adopters’ life circumstances, household income, age and employment prior to joining, and their reasons for leaving the MLM company if they were no longer active. They were also asked how many MLM companies they have joined in their lifetime and if they were currently working as a distributor for an MLM firm.

The 415 respondents who said they have never joined an MLM firm were asked if anyone had ever asked them to join, and if so, to select one or more reasons for why they declined. Both groups were asked demographic, economic, mindset, and social activity questions.

Introduction: I have chosen to complete a data analysis project for the second course option, Bellabeats, Inc., using a locally hosted database program, Excel for both my data analysis and visualizations. This choice was made primarily because I live in a remote area and have limited bandwidth and inconsistent internet access. Therefore, completing a capstone project using web-based programs such as R Studio, SQL Workbench, or Google Sheets was not a feasible choice. I was further limited in which option to choose as the datasets for the ride-share project option were larger than my version of Excel would accept. In the scenario provided, I will be acting as a Junior Data Analyst in support of the Bellabeats, Inc. executive team and data analytics team. This combined team has decided to use an existing public dataset in hopes that the findings from that dataset might reveal insights which will assist in Bellabeat's marketing strategies for future growth. My task is to provide data driven insights to business tasks provided by the Bellabeats, Inc.'s executive and data analysis team. In order to accomplish this task, I will complete all parts of the Data Analysis Process (Ask, Prepare, Process, Analyze, Share, Act). In addition, I will break each part of the Data Analysis Process down into three sections to provide clarity and accountability. Those three sections are: Guiding Questions, Key Tasks, and Deliverables. For the sake of space and to avoid repetition, I will record the deliverables for each Key Task directly under the numbered Key Task using an asterisk (*) as an identifier.

Section 1 - Ask: A. Guiding Questions: Who are the key stakeholders and what are their goals for the data analysis project? What is the business task that this data analysis project is attempting to solve?

B. Key Tasks: Identify key stakeholders and their goals for the data analysis project *The key stakeholders for this project are as follows: -Urška Sršen and Sando Mur - co-founders of Bellabeats, Inc. -Bellabeats marketing analytics team. I am a member of this team. Identify the business task. *The business task is: -As provided by co-founder Urška Sršen, the business task for this project is to gain insight into how consumers are using their non-BellaBeats smart devices in order to guide upcoming marketing strategies for the company which will help drive future growth. Specifically, the researcher was tasked with applying insights driven by the data analysis process to 1 BellaBeats product and presenting those insights to BellaBeats stakeholders.

Section 2 - Prepare: A. Guiding Questions: Where is the data stored and organized? Are there any problems with the data? How does the data help answer the business question?

B. Key Tasks: Research and communicate the source of the data, and how it is stored/organized to stakeholders. *The data source used for our case study is FitBit Fitness Tracker Data. This dataset is stored in Kaggle and was made available through user Mobius in an open-source format. Therefore, the data is public and available to be copied, modified, and distributed, all without asking the user for permission. These datasets were generated by respondents to a distributed survey via Amazon Mechanical Turk reportedly (see credibility section directly below) between 03/12/2016 thru 05/12/2016. *Reportedly (see credibility section directly below), thirty eligible Fitbit users consented to the submission of personal tracker data, including output related to steps taken, calories burned, time spent sleeping, heart rate, and distance traveled. This data was broken down into minute, hour, and day level totals. This data is stored in 18 CSV documents. I downloaded all 18 documents into my local laptop and decided to use 2 documents for the purposes of this project as they were files which had merged activity and sleep data from the other documents. All unused documents were permanently deleted from the laptop. The 2 files used were: -sleepDaymerged.csv -dailyActivitymerged.csv Identify and communicate to stakeholders any problems found with the data related to credibility and bias. *As will be more specifically presented in the Process section, the data seems to have credibility issues related to the reported time frame of the data collected. The metadata seems to indicate that the data collected covered roughly 2 months of FitBit tracking. However, upon my initial data processing, I found that only 1 month of data was reported. *As will be more specifically presented in the Process section, the data has credibility issues related to the number of individuals who reported FitBit data. Specifically, the metadata communicates that 30 individual users agreed to report their tracking data. My initial data processing uncovered 33 individual IDs in the dailyActivity_merged dataset. *Due to the small number of participants (...

For the fiscal year ending March 31, 2025, cosmetics company e.l.f. Beauty registered sales amounting to approximately *** billion U.S. dollars. This is the highest figure registered over the recorded time period. The rise to popularity Since its foundation date in 2004, e.l.f. Beauty has quickly risen in popularity, especially among younger consumers. In 2024, e.l.f. ranked first as U.S. Generation Z’s favorite cosmetics brand, and all the while its popularity in the North American country is steadily increasing. The secret to its success lies in its promotional strategies, especially through the use of social media such as TikTok, and the production of beauty products that are affordable for all consumers, as well as cruelty-free and clean. The cruelty-free market In recent years, the interest of consumers has shifted toward more natural and clean products. Since then, beauty brands have been asked to do more: in 2021, a significant share of buyers worldwide valued “no harming of animals” as one of the most important responsibilities for a beauty brand. The global market value for cruelty-free makeup is expected to increase by 2030, reaching about **** billion U.S. dollars, doubling from just ten years before.

The Data Analysis Storage Management market is projected to be valued at $5 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 12%, reaching approximately $15 billion by 2034.

"ASK partneriai", MB financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

In the "Automation survey 2001-2003" companies were asked about their motives for taking up e-commerce. Eight motives have been formulated for electronic sales. Five motives have been formulated for electronic purchasing. The companies were asked to indicate how important these motives were for the (further) development of e-commerce activities: buying and selling via the internet or another electronic network. Only the motives considered to be of great importance by the companies are included in the results. The motives whose importance was classified as "medium", "minor" or "not applicable" have not been included. The results are presented as percentages where the numerator consists of the number of companies that considered the motive in question to be of "great importance" and the denominator consists of the number of companies that make electronic sales or purchases respectively. Data available for the year 2001.

The goal of this study is to measure willingness to participate in passive mobile data collection among German smartphone owners. The data come from a two-wave web survey among German smartphone users 18 years and older who were recruited from a German nonprobability online panel. In December 2016, 2,623 participants completed the Wave 1 questionnaire on smartphone use and skills, privacy and security concerns, and general attitudes towards survey research and research institutions. In January 2017, all respondents from Wave 1 were invited to participate in a second web survey which included vignettes that varied the levels of several dimensions of a hypothetical study using passive mobile data collection, and respondents were asked to rate their willingness to participate in such a study. A total of 1,957 respondents completed the Wave 2 questionnaire.

Wave 1

Topics: Ownership of smartphone, mobile phone, PC, tablet, and/or e-book reader; type of smartphone; frequency of smartphone use; smartphone activities (browsing, e-mails, taking photos, view/ post social media content, shopping, online banking, installing apps, using GPS-enabled apps, connecting via Bluethooth, play games, stream music/ videos); self-assessment of smartphone skills; attitude towards surveys and participaton at research studies (personal interest, waste of time, sales pitch, interesting experience, useful); trust in institutions regarding data privacy (market research companies, university researchers, statistical office, mobile service provider, app companies, credit card companies, online retailer, and social networks); concerns regarding the disclosure of personal data by the aforementioned institutions; general privacy concern; privacy violated by banks/ credit card companies, tax authorities, government agencies, market research companies, social networks, apps, internet browsers); concern regarding data security with smartphone activities for research (online survey, survey apps, research apps, SMS survey, camera, activity data, GPS location, Bluetooth); number of online surveys in which the respondent has participated in the last 30 days; Panel memberships other than that of mingle; previous participation in a study with downloading a research app to the smartphone (passive mobile data collection).

Wave 2

Topics: Willingness to participate in passive mobile data collection (using eight vignettes with different scenarios that varied the levels of several dimensions of a hypothetical study using passive mobile data collection. The research app collects the following data for research purposes: technical characteristics of the smartphone (e.g. phone brand, screen size), the currently used telephone network (e.g. signal strength), the current location (every 5 minutes), which apps are used and which websites are visited, number of incoming and outgoing calls and SMS messages on the smartphone); reason why the respondent wouldn´t (respectively would) participate in the research study used in the first scenario (open answer); recognition of differences between the eight scenarios; kind of recognized difference (open answer); remembered data the research app collects (recall); previous invitation for research app download; research app download.

Demography: sex; age; federal state; highest level of school education; highest level of vocational qualification.

Additionally coded was: running number; respondent ID; duration (response time in seconds); device type used to fill out the questionnaire; vignette text; vignette intro time; vignette time.

In 2021, **** percent of U.S. marketers in companies largest than 100 employees were expected to use social media for marketing purposes. In 2013, the share stood at **** percent. Social media marketing – additional informationEveryone knows that social media started as an entertainment tool and evolved to a powerful marketing tool. While it serves its primary purpose of connecting people, at the same time it plays a major role in connecting marketers with current and potential customers. Marketing professionals agreed that social media was very important to their business. In fact, ** percent agreed with it strongly. These convictions are reflected in growing expenditures towards this medium. In the United States alone, social media marketing spending is expected to exceed ** billion U.S. dollars in 2019 – almost ** billion increase, compared to 2014.When asked about the leading challenges of social media marketing, ** percent of surveyed marketers stated that assessing its effectiveness was their main concern, followed by strategy design and analyzing obtained data. In order to evaluate the effectiveness, U.S. social media specialists employed a number of measurements. Counting the number of hits, visits or page views was the leading social media metric used in 2014. In addition, ** percent of respondents believed that the number of friends or followers on social platforms was a significant indicator of marketing success.Specific platform usage among social media professionals varies depending on the type of commerce transaction. Twitter seems to be a shared platform, ranked second in usage for both business-to-business and business-to-consumer industries. The difference is visible when considering the primary spot. Among B2C marketers, ** percent used Facebook, while among B2B marketers, ** percent indicated using LinkedIn.

The Clinical Data Analytics market is projected to be valued at $5 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 12%, reaching approximately $15 billion by 2034.

The global market size of Mobile Data Protection Solutions is $XX million in 2018 with XX CAGR from 2014 to 2018, and it is expected to reach $XX million by the end of 2024 with a CAGR of XX% from 2019 to 2024.
Global Mobile Data Protection Solutions Market Report 2019 - Market Size, Share, Price, Trend and Forecast is a professional and in-depth study on the current state of the global Mobile Data Protection Solutions industry. The key insights of the report:
1.The report provides key statistics on the market status of the Mobile Data Protection Solutions manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.
2.The report provides a basic overview of the industry including its definition, applications and manufacturing technology.
3.The report presents the company profile, product specifications, capacity, production value, and 2013-2018 market shares for key vendors.
4.The total market is further divided by company, by country, and by application/type for the competitive landscape analysis.
5.The report estimates 2019-2024 market development trends of Mobile Data Protection Solutions industry.
6.Analysis of upstream raw materials, downstream demand, and current market dynamics is also carried out
7.The report makes some important proposals for a new project of Mobile Data Protection Solutions Industry before evaluating its feasibility.
There are 4 key segments covered in this report: competitor segment, product type segment, end use/application segment and geography segment.
For competitor segment, the report includes global key players of Mobile Data Protection Solutions as well as some small players. At least 13 companies are included:
* Cisco
* Intel
* Symantec
* EMC Corporation
* Hewlett-Packard (HP)
* Sophos
For complete companies list, please ask for sample pages.
The information for each competitor includes:
* Company Profile
* Main Business Information
* SWOT Analysis
* Sales, Revenue, Price and Gross Margin
* Market Share

For product type segment, this report listed main product type of Mobile Data Protection Solutions market
* Mobile Data Protection
* Data Loss Prevention
* Mobile Device Management
For end use/application segment, this report focuses on the status and outlook for key applications. End users sre also listed.
* Application I
* Application II
* Application III

For geography segment, regional supply, application-wise and type-wise demand, major players, price is presented from 2013 to 2023. This report covers following regions:
* North America
* South America
* Asia & Pacific
* Europe
* MEA (Middle East and Africa)
The key countries in each region are taken into consideration as well, such as United States, China, Japan, India, Korea, ASEAN, Germany, France, UK, Italy, Spain, CIS, and Brazil etc.

Reasons to Purchase this Report:
* Analyzing the outlook of the market with the recent trends and SWOT analysis
* Market dynamics scenario, along with growth opportunities of the market in the years to come
* Market segmentation analysis including qualitative and quantitative research incorporating the impact of economic and non-economic aspects
* Regional and country level analysis integrating the demand and supply forces that are influencing the growth of the market.
* Market value (USD Million) and volume (Units Million) data for each segment and sub-segment
* Competitive landscape involving the market share of major players, along with the new projects and strategies adopted by players in the past five years
* Comprehensive company profiles covering the product offerings, key financial information, recent developments, SWOT analysis, and strategies employed by the major market players
* 1-year analyst support, along with the data support in excel format.
We also can offer customized report to fulfill special requirements of our clients. Regional and Countries report can be provided as well.

Planned Valentine’s Day spending in the United States was expected to reach around 27.5 billion U.S. dollars in 2025. This is an increase of about 1.6 billion dollars from 2023 and the highest spending seen. Valentine’s Day is celebrated every year on February 14th and is commonly considered the most romantic holiday of the year. Are you excited for Valentine’s Day? When asked whether they were planning to celebrate Valentine's Day in 2025, just over half of U.S. survey respondents stated they would. The number of participants in the United States tends to stay similar each year, but has decreased overall compared to a decade and a half ago. What do Americans spend their money on for Valentine’s Day? In recent years, some of the more popular gifts or experiences to give someone special on Valentine’s Day in the United States were candy and/or a greeting card. Other gifts commonly given and received, included flowers and an evening out. When it comes to the gift recipient, the majority of spending was for the buyer's significant other (or spouse), followed by other family members.

A survey was conducted in Italy in 2018 which indicated the main obstacles in digital marketing development in the country. ** percent of respondents that were working in the digital industry stated that the company’s management lacked in web culture and knowledge. ** percent of them pointed out the big confusion in metrics and marketing measurements. Digital professionals indicated other drawbacks but gave very different opinions according to the size of the business. In big companies, respondents claimed to have too much high costs related to effectiveness of campaigns, while professionals in small enterprises wished for a higher number of enterprises active in the industry.

Boost digital marketing  

Digital professionals suggested already in a survey from 2017 to change the culture of managers and integration strategies of the companies. In the same poll, ***** percent of the asked respondents stated that data analytics is a crucial aspect.

Employment in digital marketing  

Are there enough employees in digital marketing to accomplish the change? According to data from 2018, in most Italian digital companies, the staff working in digital marketing does not go over five people.

Abstract

We conduct a randomized experiment in 157 rural markets in Kenya to test how business training (the International Labour Organization (ILO)'s Gender and Enterprise Together program) affects the profitability, growth and survival of female-owned businesses, and to evaluate whether any gains in profitability come at the expense of other business owners. We work with a large sample of 3,537 firms, and use a two-stage randomization, first randomizing at the market-level, and then randomizing the offer of training to individuals within treated markets. A year and a half after the training has taken place, half of the sample assigned to training was then offered a subsequent mentoring intervention intended to test whether additional group-based and in-person support strengthens the impacts of training. Four rounds of follow-up surveys with low attrition are used to measure impacts at one and three years after training. This is complimented with data from a market census taken four years after training, that also included male-operated firms.

Geographic coverage

Kakamega and Kisii counties in the Western region, and Embu and Kitui counties in the Eastern region.

Analysis unit

• Individual female microenterprise
• Firm

Universe

Women operating in markets in four counties in Kenya: Kakamega and Kisii in the Western region, and Embu and Kitui in the Eastern region

Kind of data

Sample survey data [ssd]

Sampling procedure

The selection of the study areas was the result of a participatory process that involved the Technical Committee of the ILO Women Entrepreneurship and Economic Empowerment (WEDEE) project as well as other relevant stakeholders. A Stakeholder retreat in October 2012 was used to pre-select 10 counties from the 47 counties in Kenya as possible locations for the study. A more detailed review of these 10 counties and consultations with the stakeholders were then used to select 4 counties in which to provide the ILO Gender and Entrepreneurship Together (GET Ahead) training: Kakamega and Kisii in the Western region, and Embu and Kitui in the Eastern region.

In each of Kakamega, Kisii, Embu and Kitui counties field staff from Innovations for Poverty Action, Kenya, mapped out all market centers deemed as medium or large outside of the main cities. Field staff then conducted a market census, applying a 31-question listing questionnaire to each female-owned enterprise operating on a non-market day in these markets. This questionnaire took a median time of 15 minutes to complete, and collected data on business type, education, age, profits and sales, membership in women's associations or merry-go-rounds, and contact follow-up information. The listing operation took place one county at a time between June 3, 2013 and November 1, 2013.

After the census, three markets in Kakamega county were dropped because the number of women in these markets was too few. Researchers then applied an eligibility filter to determine which women to include in the baseline survey. This filter required the women to have reported profits, and not to have reported profits that exceeded sales; to have a phone number that could be used to invite them for training; to be 55 years old or younger; to not be running a business that only dealt with phone cards or m-pesa, or that was a school; that the person responding not be an employee; that the business not have more than 3 employees; that the business have profits in the past week between 0 and 4000 KSH; that sales in the past week be less than or equal to 50,000 KSH; and that the individual had at least one year of schooling. These criteria were chosen to reduce the amount of heterogeneity in the sample (thereby increasing our ability to detect treatment effects), and to increase the odds of being able to contact and find individuals again.

Applying this eligibility filter reduced the 6,296 individuals to 4,037 individuals (64%). Out of a target of 4,037 individuals, the team was able to interview 3,538 (87.6%) in time to consider them for inviting to training.

Randomization process

The individuals who had satisfied the screening criteria and completed the baseline survey were then assigned to treatment and control in a two-stage process:

First, markets were assigned to treatment (have some individuals in them invited to training) or control (no one in the market would be invited to training) status. Randomization was done within 35 strata defined by geographical region (within county) and the number of women surveyed in the market.

Then within each market, individuals were assigned to treatment (be invited to training) or control (not be invited to training) within treated markets by forming four strata, based on quartiles of weekly profits from the census (<=450, 451-800, 801-1500, 1501-4000), and then assigning half the individuals within each strata to training. When the number of individuals in the strata was odd, the odd unit was also randomly assigned to training. This resulted in 1,173 of the 2,161 individuals in treated markets being assigned to treatment, and 988 to control groups.

Additoinal details on sampling are abailable in Section 2 of the Working Paper provided under Related Materials.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The following survey instruments were used for data collection: - Census of Women Entrepreneurs - Baseline Questionnaire - Long Follow-up Surveys (Rounds 2 and 4) - Short Follow-up Surveys (Rounds 3 and 5) - Market Census Questionnaires (Rounds 2 and 4) - Final Market Questionnaire - Customer Survey Questionnaire

The Market census questionnaire took a median time of 15 minutes to complete. It collected data on business type, education, age, profits and sales, membership in women's associations or merry-go-rounds, and contact follow-up information. The baseline questionnaire took a median time of 90 minutes to complete. The 30-page questionnaire asked detailed questions about the business owner, her family and business activities.

Response rate

Overall we were able to interview 95.0 percent of the sample in at least one of round 2 or 3, and 92.3 percent in at least one of round 4 or 5. In addition, in cases where we were unable to interview someone due to refusal, travel, death, or other reasons, we collected information from other household members or close contacts on whether the individual in our sample was currently operating a business. This enables us to have data on survival status for 99.3 percent of the sample at one year, and 97.2 percent at three years. There is no significant difference in data availability with treatment status at the three year horizon, although those assigned to treatment are 1 to 2 percentage points more likely to have data available at the one year horizon. See Appendix Table 2 of the working paper provided under Related Materials details response rates.

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