10 top skills of the best Innovation Managers

By definition, an innovation role covers many different areas of expertise. We’ve listed the most prominent skills you might be looking for on the resume of job candidates. A T-shaped profile will probably be the one you’re looking for. You need an emphatic generalist that can quickly jump from one domain to the other.

Example interview questions

For each, we’ve added a couple of example questions. Use this list to write your own interview script. 

1. Business Case oriented
Working with limited info should not be a problem. An innovation manager should be able to work with a brief list of assumptions to build the basic of an innovative business case.

• What could be the selling price of Virtual Reality Goggles for surgeons?
• What would need the biggest investment: Building & maintaining a support chatbot or hire an extra support colleague?

2. Project oriented
Many innovation managers have to follow up on dozens of innovation projects. Make sure to look for people with a decent level of project mgmt experience. This person will have to manage your innovation process. You don’t want a creative dreamer at the wheel, right?

• Which project management tools or services have you used before. Why (or why not) were those tools the best solution for the task at hand?
• What is the most difficult part of managing a new business or innovation project? 

3. Manage failure and exceptions
The more disruptive the project, the bigger the chance failure can occur. You’re walking in new territory, so an innovation manager will need to prepare others around her that future is always very uncertain.

• What is the biggest threat to an innovation project? How to manage this risk?
• How many client/users would you talk to before going public with a new innovation?

4. Willing to change
Talking about change is not enough. Look for candidates that are willing to take the first steps themselves. By doing so, they can inspire others to follow.

• When and how did you initiate a new idea that was out of the comfort zone for most people?
• Name a situation where you managed to change how your organization worked? 

5. Curious
Look for people that are eager to find new information. People that want to deep dive into new domains.

• Tell me something most people don’t know about? 
• Do you follow niche websites, people or sources about a very specific topic?

6. Optimistic
Innovation leads will need to see opportunities everywhere. Obstacles are just a trigger for change.

• Name a failed innovation but that you could relaunch in the market? Explain how.
• When would you stop an innovation program or project?

7. Fast and Furious
Thinkers that take 2 years develop an idea are probably not the leaders to steer an innovation team. You need people that will look for shortcuts. People that understand that done is better than perfect.

• Have you ever launched a project that you’re ashamed of today? Why?
• What was the fastest cycle you ever experience to go from the first idea to public launch? What was your role in this initiative?

8. Cross-industry experience
Ideally, an innovation manager has a mixed background with practical experience in multiple industries. The best ideas emerge when different views collide.

• When did you solve a problem in your organization, based on info you learned elsewhere? 
• How do you keep yourself up to date, what sources do you follow? (generalist will mention a mix of domains)

9. Able to manage internal politics / buy-in 
A corporate is packed with internal politics. Innovation projects are the perfect fuel to spark internal fires. An innovation manager will need to spend a lot of time on internal communication.

• Explain a situation where an important project got blocked in the ‘red tape’ of your organization. What did you do to move this project forward?
• When working on a new innovation project, who needs to stay in the loop? (other roles/departments/…) Who needs to stay out?

10. Strategic Sighted
Innovation project fall by definition out of your companies comfort zone. Can this person inspire others with a clear vision where else the company needs to look?

• Can you give an example of a promising technology that most people see as the next big thing, but that you don’t agree with?
• Which small or medium-sized company has a strong innovation strategy & why? (avoid that people just talk about Apple, Facebook, Google or Amazon)

https://goo.gl/ZXswpL

Thinking Outside The Box

In digital, it’s all about personalization. Isn’t it time pharma caught up with other industries?

By Adam Chapman

Soon, the pharma industry will face a stern ultimatum – diversify or die. The internet – that majestic marketplace of ideas – has been busy empowering consumers through unfettered access to information and services. Now that customers know what they want (and when they want it), holding their hold their attention means tracking and reacting to the minutiae of their online movements. 

This sophisticated idea is at the center of Volkswagen’s marketing model, says Adel Baraja, Sales and Marketing Consultant at Volkswagen, who believes a high level of personalization will provide exponential growth in the pharma industry.

“Marketing channels like outdoors, out-of-home, pay-view or TV deliver one message that aims to suit everybody,” he says. “In digital, we are trying to give our customers or prospects a totally different message.”

It’s mass media but it’s also custom, he says. “We highlight people's interests, from either their browsing behavior or online habits, and we identify the people in the marketplace who want to buy a car. In a way, it’s not invasive at all; Netflix or Amazon make suggestions based on books read or programs watched in a similar way.” 

In tracking a customer’s online activity, connectedness is the starting point, says Baraja. “We are looking for the connected customer, people that use their phone or multiple devices to get online. Those are the ones that are always searching, always looking up items – recipes, advice, do-it-yourself guides.”

Where does Volkswagen mine this data? “We work with multiple agencies and service providers, who gather and analyze all this big data. We do not have to do it manually, we can’t even see them – the data or the people – as it's anonymous. We just know that they have set the criteria that we are looking for,” he says.

Pooling data from third-parties can provide a general overview of behavioral patterns, then Volkswagen uses sophisticated metrics to tailor its message to each individual visitor on its website. “We know how these customer are coming in, for example, if they clicked on a banner, but we also know now – through advanced algorithms – that somebody browsing a website has an attention span of eight seconds – that’s less than a goldfish. They get distracted by another website or by a TV set and they leave your website,” says Baraja.

Many marketers would give up at this point – after all, they’ve served the ad, had a click and a visit, and formed a bond. “We keep connecting to that same client; we might be able to serve him a different ad. If he has seen the exterior of the car, we serve him another ad inviting him to see the interior,” he says, adding that if a customer leaves the website midway through customizing a car, a third ad can then be deployed on a third-party website asking them to come back and complete the customization. “The whole idea is to re-target the same customer again and again because they are the highest potential, rather than focusing on a new one with the least potential.” 

Baraja rejects the idea that this approach would not work in pharma. “A lot of people say: If people are healthier, pharmaceutical companies are going to lose money. But look at cooking shows; chefs show you their recipe and they show you how to make the food, but you will still go to the restaurant to eat it. It doesn't mean that if you are being helpful, showing them how to be healthy, people will stop coming back to you. They know you, you will become the one they take advice from, they will listen to you. If you throw anything at them they will buy it because they trust you.”

The right attitude and internal awareness are essential to successfully implement a personalized digital strategy. The increasing ubiquity of the digital world must be mirrored by the marketing strategy, he says. “Digital is no longer a separate strategy to the sales strategy, conventional marketing strategy or branding strategy. It is one strategy that takes a different direction, developing and evolving.”

The impact across the organization requires a top to bottom understanding, he says. “Marketing has become involved in IT – there is a lot of software and processes involved and it will continue to become more sophisticated. We will always have a CEO that understands where both digital and marketing is going. A CEO that also supports the vision and understands this is the requirement,” says Baraja.

Data-Driven Transformation: Accelerate at Scale Now

Data-driven transformation is becoming a question of life or death in most industries. But initiatives to embed data in operations throughout a company often fail. This is because companies start by trying to reinvent their core IT systems—a multiyear effort that can run to hundreds of millions of dollars. Sadly, most of this money is wasted, because these massive centralized efforts take far too long. When the rules of business are being rewritten on a quarterly basis, companies need an approach to transformation that is agile, focused on results, and manageable.

Most CEOs recognize the power of data-driven transformation. They certainly would like the 20% to 30% EBITDA gains that their peers are racking up by using fresh, granular data in sales, marketing, supply chain, manufacturing, and R&D. And they may even dream of joining the ranks of data-driven companies that have shoved aside traditional players among the world’s most valuable companies. (See Exhibit 1.)

Yet CEOs are right to wonder how their organizations—where managers and executives already complain about a lack of data skills and where overburdened IT systems seem unlikely to be able to handle a tenfold increase in company data—can pull off such a transformation. These CEOs want to find a reliable way to move their companies into the data-driven future so that they can set up their companies to survive—and not put them in danger in the process.

There is a better way to approach data transformation. In our experience, these initiatives can succeed only if they are cost effective, incremental, and sustainable. Transformations should start with pilots that pay off in weeks or months, followed by a plan for tackling high-priority use cases, and finishing with a program for building long-term capabilities. Working with clients across industries, we have developed a three-phase approach to data-driven transformation. It starts with small-scale, rapid digitization efforts that lay the foundation for the broader transformation and generate returns to help fund later phases of the effort. (See Exhibit 2.) In the second and third phases, companies draw on knowledge from their early wins to create a roadmap for companywide transformation, “industrialize” data and analytics, and build systems and capabilities to execute new data-driven strategies and processes.

This three-step approach is faster, less costly, and more likely to succeed than a system-wide overhaul. Using existing data systematically and combining it with external data (from social networks, for example) for marketing or customer issue resolution can deliver fast results. We have seen companies achieve 15% to 20% of the potential of a full data-driven transformation in six to nine months.

Use quick wins to learn and fund the digital journey. In this first phase, companies identify the low-hanging fruit—discrete, rapid digitization efforts that can deliver quick wins. These projects immediately move the needle on performance in a key area—sales support or supply chain, for example. And rather than taking years, implementation occurs in months and starts paying back almost immediately. The pilot projects show that the company can benefit from digitization, and they provide important lessons in how to roll out digital transformation across the company. Crucially, the extra value that the quick wins create can help pay for longer-term efforts, potentially making the transformation self-funding.

Design the companywide transformation. In the second phase, which can begin while the first initiatives are still underway, the company draws a roadmap for company-wide transformation. This involves building a portfolio of opportunities—identifying and prioritizing functions or units that can benefit most from transformation. It also involves locating and starting to address roadblocks to transformation. During the design phase, companies also invest in framing and communicating the vision for the transformation to build support for needed changes, and they invest in systems to industrialize data analytics—making analytics a resource for every operation.

Organize for sustained performance. With a detailed roadmap in place and with the experience and funding available from the early projects, the company is ready to undertake a full-fledged digital transformation. In this phase, digital and data-driven processes and work methods spread to every corner of the company. Employees learn to work across silos to enable data-driven processes, and leaders make the organizational changes necessary to sustain the new approaches. The company creates a data-driven culture by investing in capabilities to use analytical insights and by launching a change management program to embed new mindsets, behaviors, and ways of working.

USE QUICK WINS TO LEARN AND FUND THE DIGITAL JOURNEY

Moving a big company in a new direction is a huge challenge for management. The best-conceived and most urgent transformation programs—digital or otherwise—are sometimes no match for organizational inertia. This may explain why 70% of publicly announced transformation programs fail to meet the company’s ambition, its timeline for the transformation, or both.

But large organizations can overcome resistance and build the enthusiasm needed for change to succeed if they approach transformation in the right way. By starting a transformation journey with a small number of quick initiatives that demonstrate what can be achieved by using new approaches, companies greatly increase their chances of eventual success.

Leaders should choose quick-win initiatives carefully, on the basis of several critical criteria: they must have a high chance of success, a significant and rapid payback, and visibility across the company. A major industrial company, for example, started by digitizing high-profile processes, including inventory management. (See “Building Momentum Through Pilots.”)

BUILDING MOMENTUM THROUGH PILOTS

A large industrial company contemplated a massive digital transformation to increase its efficiency and to compete more effectively in markets where producers have limited pricing power. The company did not want to tie up capital in a massive change program and wait years for a payback. To avoid that outcome, the company first identified a few quick-win initiatives that could pay off within a month or a quarter.

The first initiatives it selected were in inventory management and capacity optimization—analyzing output and shifting production to sites that made the most profitable products. For these quick wins, the company used static data and created one-off solutions. But the projects led to significant savings and more sales of high-profit items, which generated immediate value. In nine months, quick wins generated $20 million in value. Once the projects based on static data were up and running, the company went back and built the systems it needed to manage these processes and functions continuously, using real-time data flows.

Applying the lessons from its early wins, the company has created a roadmap for ten major data transformation initiatives in areas as varied as demand forecasting and managing the outbound sales force. The company has also made plans for new companywide resources to support data-driven approaches and make them sustainable, including building a data lake. And it has begun identifying new data-driven business models. The overall goal for the transformation is to unleash $200 million in value over three to five years and to help the company raise its EBITDA margin by 2% to 4%.

Initial projects may be limited in scope, but it is essential that they succeed and serve as a convincing advertisement for the benefits of digital transformation. For this reason, companies should not only choose projects carefully but also be pragmatic about execution. It is best to avoid projects that would require fundamental changes in data handling—projects that would entail building a new data repository, for instance. Companies should use agile methodologies to build any new analytics models, with short sprints and tight timelines for developing a minimum viable product that can be tested and used to define additional requirements and refinements.

Quick-win projects should require no more than four to six months to complete, and their value should be demonstrable within weeks. During the quick-win phase, companies can build their ability to focus and execute swiftly and to work across silos—critical capabilities for pursuing large-scale transformation efforts. Quick wins can also energize and inspire managers and employees who have seen change initiatives bog down in the past.

DESIGN THE COMPANYWIDE TRANSFORMATION

As soon as it is clear that the early digital transformation projects are off to a solid start, the company can start preparing the roadmap for extending digital transformation across the enterprise. This starts with a high-level vision, which company leaders translate into a portfolio of initiatives (or use cases) to be rolled out in a logical order, on the basis of factors such as size of impact and competitive needs or opportunities. Then the company must agree upon some underpinnings of digital operations—analytics, data governance, and data infrastructure. (See Exhibit 3.) Creating a roadmap for use cases and projects to build data infrastructure and other resources needed for data-driven operations can not only make the transformation run more smoothly but also ensure that these investments pay. (See “Driving Fast Value from Data Transformation in Logistics.”)

DRIVING FAST VALUE FROM DATA TRANSFORMATION IN LOGISTICS

For more than 30 years, a major global logistics company led its industry in the use of information technology, and its leaders believed that data was its competitive differentiator. The company took the long-term view and had spent more than five years implementing a new ERP system that cost hundreds of millions of dollars. But after massive investments of time and money, the company could not demonstrate that it had gained any competitive advantage in cost or revenue.

Then the company took a new, agile tack. This time it created a detailed roadmap for transformation based on two primary considerations: an examination of the data needed monthly, weekly, and in real time to optimize functions or operations and to generate the most impact for the company; and an assessment of the systems and data already available to fill the newly identified business needs. On the basis of this roadmap, the company began a series of pilot projects, using benchmarking data to optimize important cost drivers such as fuel consumption, maintenance, and labor. Another project aimed to improve pricing performance by accessing data such as customer P&L through new analytics.

Over the course of three years, the company systematically completed the list of projects for every major value driver in the business. After dozens of projects in areas such as pricing, fuel consumption, and network, the company went from sitting in the middle of the pack in operating performance to becoming the industry leader on EBIT performance.

Before attempting to define its vision, a company needs to have a thorough understanding of where it stands in terms of data, digitization, and current capabilities. As a preliminary step, then, the company should quickly and objectively assess its situation and gauge how its capabilities stack up against best practices in its industry. One option in this area is a diagnostic developed by BCG that weighs 21 factors in assessing a company’s starting point in data and analytics capabilities and assets, backing up the assessment with extensive, continually updated benchmarks.

Five Critical Steps for a Successful Data Transformation

The assessment of data capabilities gives the company the information it needs to carry out five critical steps.

Build a vision. When planning a data-driven transformation, a company must set the appropriate vision for its business. For some companies, the transformation will mostly be about using data to improve operations and to compete more effectively. For others, it might involve building new business models. The visioning exercise should include identifying the macro use cases—the most important projects that the company wants to undertake.

Select the portfolio of initiatives. Using its vision and its list of macro projects for reference, companies can create a full list of transformational initiatives. The company should use a structured ideation process to compile the list, and it should use a rigorous prioritization methodology to set the schedule. Factors such as data availability, regulatory compliance, and technical or modeling difficulty, as well as dollar value, customer benefits, and strategic importance must also be weighed.

Devise an analytics operating model. Before investing in new data analytics capabilities, a company should specify how it wants the data analytics function to work. After analyzing its internal capabilities, it can decide which components of the analytics function to seek in-house and which to outsource.

Establish data governance. To ensure the quality and integrity of the data it will use for business decisions—with and without human intervention—a company must have strict governance rules and a data governance structure. It must also define data quality and establish ways to continually improve it.

Define data infrastructure. A company moving toward data transformation should address the following questions: Can our current infrastructure support our future data value map? Should we make or buy? Should we go to the cloud? Do we need a data lake? What role should our legacy IT systems play in our data transformation? The company should design a data platform (or data lake) that can accommodate its product map and should use that platform to progressively transform its legacy systems.

Industrialize Data Early to Ensure Full Transformation and Long-Lasting Impact

While the company continues to sketch the transformation roadmap—if not sooner—it needs to begin industrializing its data and analytics. This means setting up a way to standardize the creation and management of data-based systems and processes so that the output is replicable, efficient, and reliable. Digital systems are the new means of production, and they need to have all the attributes of industrial machinery, including reliability and consistency. Above all, the company needs to have a way to guarantee that it generates and harnesses high-quality data and has an efficient data environment.

A centralized or hub-and-spoke operating model can ensure clear, consistent strategy and execution; rationalize investments; and ensure economies of scale. Business units and functions that will rely on new data-driven systems and processes should have input into system design and data quality assurance, but these groups must rely on the core data management organization for data governance.

The second element in industrializing data consists of determining the appropriate architecture to support data analytics across the organization. A flexible open architecture that can be updated continuously and enhanced with emerging technologies is generally the best option. Rather than embracing an end-to-end data architecture, companies should adopt a use-case-driven approach, in which the architecture evolves to meet the requirements of each new initiative. The data governance and analytics functions should collaborate to create a simplified data environment; this will involve defining authorized sources of data and aggressively rationalizing redundant repositories and data flows.

ORGANIZE FOR SUSTAINED PERFORMANCE

As is the case with any change program, the success of a data transformation is measured by sustained results—and those will not materialize without making the company and its culture data centric. To prepare its organization for a digitized future, the company needs to move on four fronts: creating new roles and governance processes, instilling a data-centric culture, adopting new ways of working, and cultivating the necessary talent and skills.

Many companies may be capable of managing this change on their own; but if a company faces competitive challenges that require a rapid transition, or if it is far behind in digitization or lacks the resources and capabilities to manage the transformation, it may benefit from adopting a build-operate-transfer model (which we discuss below). This involves creating a dedicated organization—usually run with the guidance of an outside expert partner—that takes over the organizational change effort.

Define new roles and governance rules. To ensure the sustainability of the benefits it obtains through the adoption of new digital processes, a company needs to make clear who has responsibility for building and running new systems and maintaining specific types of data—and how to manage those people. The changes begin at the top: senior leaders should adopt data-driven objectives and cascade those goals throughout the organization. Top management may want to set up data councils to extend the work to all sectors of the organization and to carry it out more effectively. The company should promote data awareness by using data champions to disseminate data-driven practices. The company can set up a change management function under the chief data officer, too. The data awareness effort should extend to all work, including tasks that the digital transformation does not directly affect. For example, the company might create data-based metrics for functions such as HR, perhaps measuring the number of applications processed per job filled.

Build a data-first culture. Not everyone needs to become steeped in data analytics or learn to code in order for digital transformation to work. However, everyone does need to adopt a less risk-averse attitude. To move quickly and to continually find new ways to apply data, companies should behave a bit like software development operations, embracing a test-and-learn culture that encourages experimentation, accepts—even celebrates—failure, and is always learning. Companies can also encourage the desired cultural change through organizational moves, such as creating internal startup units where employees can focus on experimentation or co-locating data labs within operating units. The company can also promote the new culture by using cross-functional teams that share data across silos, thereby encouraging openness and collaboration throughout the organization.

Adopt agile ways of working. The entire organization does not have to become expert in agile, but the company can adopt many of the tactics of the agile method and use them in everyday operations to increase the organization’s responsiveness and adaptability. It can establish scrum teams with squads and tribes to tackle specific problems—and accelerate the pace with weekly sprints, rather than months-long efforts. Teams and groups can implement morning standups and weekly demos (reviews) as part of governance. Overall, the new ways of working should emphasize autonomy and reduce hierarchy.

Cultivate the necessary talent and skills. For data-based transformation to work, the company must have talent with the right skills to execute data-driven strategies and manage data-based operations. This presents a workforce planning challenge, starting with assessing current employees and defining future needs. The company should create an inventory of the talents and skills that its employees will need, and it should identify where the gaps are in the current workforce. Companies will need to retrain current employees, hire new talent, or use a partnership to get the right capabilities. To recruit people with digital skills, the company may need to rethink the value proposition it offers—work, opportunity, rewards, career path, and so on—in relation to what tech companies offer.

Consider the build-operate-transfer model. In some instances, a company may need to adopt bolder steps to accelerate its data-driven transformation. This may be because it is starting from far behind its competitors or because it lacks the capabilities and resources needed to drive the transformation internally. In such situations, using the build-operate-transfer model makes sense. Adapted from the construction industry, this model involves creating a stand-alone organization in partnership with an outside vendor that has the expertise to run transformation initiatives. The organization focuses on managing transformation efforts and is staffed by employees from both the firm and the outside partner. It takes responsibility for setting up and running the use case projects and other elements of the transformation. Over time, as projects are completed, the partner withdraws its employees. Eventually it transfers all work and resources back to the company, and the stand-alone organization dissolves.

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The promise of data-driven transformation has captured the imagination of leaders throughout the business world and is driving change in the public and social sectors, too. Executives are inspired by the idea of using data to make better decisions and digitizing all sorts of processes to improve performance. They are also motivated by fear that they won’t be able to keep up with competitors who are ahead of them in data-driven digital transformation. These forces can encourage companies to try to achieve sweeping, companywide change to go digital—which can lead to counterproductive overreaching. This contest will not be won by making huge bets. The winners will be agile, pragmatic, and disciplined. They will move fast and capture quick wins, but they will also carefully plan a transformation roadmap to optimize performance in the functions and operations that create the most value, while building the technical capabilities and resources to sustain the transformation.

Antoine Gourévitch , Lars Fæste , Elias Baltassis , and Julien Marx

Как назвать цену товара клиенту

Многие менеджеры, разговаривая с покупателем, приходят в ступор, когда их спрашивают о стоимости товара. Они не знают, что ответить в таких случаях, т. к. цена может попросту отпугнуть клиента и сделка не состоится. Для решения этой проблемы существует ряд эффективных техник, которые необходимо вписать в сценарий телефонного разговора в продажах. 

Техники при обозначении цены 

Самая известная и эффективная техника обозначения цены – это, пожалуй, «Бутерброд». На чем она основана? Когда вы общаетесь с человеком он запоминает в основном, то что вы говорите в начале и в конце – это особенности психики.

Техника «Бутерброд» Основана на том, что цена закладывается внутрь вашей презентации. К примеру, как эта техника может работать в оконной компании: «Вы решили заказать 1 окно на кухню в панельный дом, комплектация «под ключ». У вас есть возможность приобрести его за 10000 рублей. Вы получаете широкий подоконник, на который вы сможете поставить цветы, москитную сетку, которая защищает вас от мух. Наши монтажники приедут, вытащат старую раму, установят новую, и плюс мы окажем дополнительную услугу «вывоз мусора», все верно?»

Так же при обозначении цены существует правило, что Сначала вы должны называть стоимость без скидки, а потом со скидкой. Как часто вы слышали такую вещь, когда вам менеджер или какой-нибудь продавец обозначает стоимость, а вы говорите: «А скидка?» А вам в ответ: «Это уже со скидкой». И в этот момент появляется такое ощущение, что тебя только что обманули – неприятно. Такого быть ни в коем случае не должно. Так же не следует говорить, что скидок нет, у вас обязательно должна действовать скидочная система, это привлекает покупателей.

Следующее правило, при обозначении стоимости, Мы не должны употреблять такие слова, как «купить, потратить» и прочее. Это предполагает трату, а люди не любят расставаться с деньгами, они любят их вкладывать. У вас должно быть некое предположение инвестиций. К примеру, слово «приобретение» предполагает то, что человек вкладывает деньги в свой дом. Он получает: тепло, комфорт, уют и прочее. Ни в коем случае нельзя говорить: «Вы можете купить, вы потратите на это всего 500 рублей».

Так же при обозначении цены можно использовать Технику «вилка цен». Это предполагает, что вы должны называть несколько цен на разные комплектации, причем начиная с самой дорогой.

Как правило, клиент будет выбирать нечто среднее. Плюс ко всему вы можете дать скидку. «Вилка» достаточно хорошо работает. Например, клиент звонит и спрашивает: «Мне необходимо, окно на кухню в панельный дом, все «под ключ», сколько оно будет стоить?». Вы отвечаете: «Комплектация «Черная икра» у нас стоит 25000 рублей, комплектация «Доступное тепло» - 12000, и «Дешевый фарш» - 8000 рублей». В этом случае клиент, скорее всего выберет доступное тепло.

Следующая техника – «Долларизация». Когда вы рассчитываете выгоду клиента в денежном эквиваленте. К примеру, если вы занимаетесь продажей окон, можно сказать: «При установке энергосберегающего стеклопакета, вы получаете возможность экономить на отоплении», - это реально так. Если мы говорим о счетах на газ, то они сокращаются где-то на 30%.

Так же можно «разбивать» цену на срок службы товара. Техника «разбивание цены». К примеру, если клиент говорит, что окно у конкурента стоит на 3 тыс. дешевле, вы объясняете клиенту: «Знаете, разницу в 1000 рублей я могу понять, и мы готовы дать вам такую скидку, но разница в 3000 рублей, наверное, должна вас насторожить. Срок службы качественных окон, а у нас они именно такие около 40 лет, это 480 месяцев, получается, что наше окно стоит 20 рублей в месяц. А то стоит 15 рублей в месяц, но еще не известно, сколько оно прослужит. И где здесь экономия?»

В этом случае он понимает, что доплатив всего 5 рублей в день, может получить спокойствие, тепло, комфорт на десятки лет. Все эти методы можно использовать в сценарии телефонного разговора в продажах.

100 Websites That Rule the Internet

​​​Megatrends changing the world as we know it​​​

​Disruption affecting our lives and businesses is accelerating at an unprecedented rate. Today any movie we want to watch is just a few clicks away. We can buy a new pair of shoes, get takeaway, hire a cleaner, by simply using our smart phones and have it conveniently arrive at our door. Businesses like Spotify, Uber and Facebook are thriving while many well-known brands such as Blockbuster and Borders have disappeared. Here are the six global megatrends that will continue to change the world in 2017 and beyond.  

1. Countries growing and changing​

Geopolitical uncertainty will​ continue in 2017. We will see more dramatic changes increasing instability and fragm​entation, as we have seen last year with Brexit and the US presidential election. Currency fluctuations, differing regulatory environme​​nts and varying consumer views will provide challenges to businesses. More disruption and opportunities will be created from emerging economies in Asia, Latin America and Africa. Increasing middle class means higher spend on services, increasing competition and opportunities to create new markets. In dealing with this ever changing landscape, organisations need to manage risk through diversification, while becoming more nimble, and most importantly having a clear strategic focus.

2. Change in demographics

Population is growing at an unprecedented rate. In 2009 there were 7.4 billion people and by 2040 it is projected to be 9 billion. People will live longer and the ageing will outweigh the working population. By 2050 there will be only two working age people for every elderly person. There is an opportunity for businesses to encourage people over 50 to have greater participation in the workforce by developing digital skills and increasing workplace and retirement flexibility.​

3. New Technologies

As artificial intelligence accelerates, many industries will be at risk of automation. This will see future workforce focus on tasks requiring human strengths such as reasoning and emotional intelligence. Growth in the wearables market will continue and will be more entwined in everyday fashion. Conductive fabrics or sensor-clad smart garments will become more wide spread, while ingestibles will gather mass amounts of information on our health. 
Customer expectations will continue to increase with the acceleration of the Internet of Things (IoT). 50 billion devices are expected to be connected by the year 2020. The future is 'smart homes and cities' with near limitless network connectivity of everyday objects such as phones, appliances and cars. Organisations that are able to connect experiences through all touch points will continue to differentiate and engage consumers.

4. Customer experience and expectation​

Customer's expectations continue to increase. Experiences that surround a product or service are as important as the product itself. They expect these experiences to be personalised, consistent and available across all channels, both digital and analog, from anywhere at any time. A unified view is essential. Success depends on how well these channels are organized and connected to deliver a seamless experience.

Customers expect to be treated as individuals and have everything they need, when they need it. Their ability to self-service and customise the experience is crucial as they want to be more informed and in charge of how they engage. Not every customer wants to come to you for simple queries, and prefer to learn and help themselves. As a result businesses need to provide flawless support 24/7.

5. Big Data

'Big Data' will continue to play a key role in business' ability to deliver great customer experiences. The volume of data will grow even more with the increase in new technologies such as wearables, mobile devices and the IoT. However having big volumes of data isn't necessarily better. It's what businesses do with the data that's important. An opportunity exists for businesses to translate the data into actionable insights. One of the biggest expectations from customers will be receiving answers or an approval for an application straight away using real-time data collection and analysis. Businesses that are not quick to respond will lose new and existing customers to competitors.

6. New Business Models

Today new business models and disruption almost go hand in hand. In the last few years we have seen the rise of new models including:

Subscription and shareconomy based models which have created new forms of revenue generation. Think Netflix, Uber, Airbnb and HelloFresh.

The freemium model, which allows customers to have a basic service for free and then pay to upgrade, has allowed businesses to build a large customer base. We have seen this implemented successfully by Dropbox, LinkedIn and Spotify.

The democratisation of coding, crowdsourcing, peer-to-peer lending have enabled numerous start-ups with great ideas to take-off. More innovative and disruptive ventures are expected to develop, disrupting businesses globally. Businesses need to continuously be at the forefront by becoming disruption thought leaders.

At its core, the societal and technological shifts above are resulting in two things:

1. A consumer revolution sparked by their connectivity to everything, anywhere and anytime. As a result of which customers demand constant, convenient and personalised experiences

2. Tremendous changes in the ways businesses are conducted to stay ahead in the growing market competition

Regardless of your personal opinion these changes will and are changing the face and pace of business today. The question then remains is how your business can continue to increase their scale, offer security and stability, while being nimble and able to predict and respond to market opportunities.

Connecting the dots and being able to navigate change starts with awareness. Computershare has worked with many organisations on creating an appreciation of these macro trends and their implications, alongside solutions to ensure a relevant place for our clients now and in the future.​

GENERAL MANAGER DIGITAL & SOLUTIONS

Lisa McAndrew

Формирование деловых взаимоотношений и подарки: Япония

В Японии принято обмениваться подарками среди коллег. Существует традиция взаимности. В соответствии с теорией Герта Хофстеде японцы имеют очень высокий индекс дистанцированности от власти и придают большое значение положению в обществе. Подарки служат знаками внимания и могут использоваться как проявление благодарности за гостеприимство или признания положения в обществе. Подарки могут дарить на встречах, обедах, ужинах и даже во время переговоров. Поскольку подарки являются важной частью японской культуры, необходимо уметь их дарить.

Японцы часто проявляют консервативность и предпочитают знать, когда они получат подарок. Чтобы не смущать принимающих Вас людей, никогда не преподносите им сюрпризы. Сообщайте о планируемом подарке заранее. Лучше сделать подарок в конце визита, а не в середине. Если сделать наоборот, может создаться впечатление, что Вы ожидаете взаимности в ответ на Ваш жест. Японцы будут очень стараться Вам угодить, поэтому старайтесь не слишком восхищаться чем-то, что принадлежит японцу. Он может почувствовать себя обязанным подарить Вам эту вещь.

Если Вы осведомлены о предстоящем обмене подарками, подготовьтесь к этому заранее. Хорошими подарками считаются говядина высшего сорта, отборные фрукты или вина, бурбон или другое виски. Если нужно сделать подарки нескольким людям, выбирайте подарки в соответствии с положением получателей. Таким образом Вы можете ясно сообщить о своем понимании и признании иерархии в японском бизнесе.

Обладая высоким индексом избегания неопределенности, японцы обычно придают большое значение символизму и ритуалам. Важен не подарок, а манера его дарения. Обязательно упакуйте подарок. К выбору оберточной бумаги следует подойти с большой ответственностью. Неяркая однотонная наиболее предпочтительна. Не следует использовать белый цвет, поскольку он символизирует смерть. Используйте красный цвет для особых случаев, таких как день рождения или свадьба; в общем же случае следует избегать слишком ярких цветов и лент.

Вручайте подарок, держа его обеими руками, таким же образом принимайте подарки. Это символизирует Вашу благодарность за подарок и, что самое важное, Ваше уважение к дарящему. Как правило, подарки не распаковываются в присутствии дарящего. Так принято во избежание смущения в случае, если подарок неудачен или, наоборот, слишком роскошен. Если Вы должны открыть подарок в присутствии дарящего, будьте осторожны, чтобы не порвать оберточную бумагу. Разворачивайте ее осторожно, это покажет Ваше уважение к тому, кто упаковывал подарок. Важно выразить благодарность, держась при этом почтительно и сдержанно. Не обнимайте дарящего, поскольку открытое проявление признательности недопустимо. Можно поклониться в знак благодарности.